L Courtial-Manent
email: lea.courtial-manent@univ-grenoble-alpes.fr

Jean-Louis Mugnier

S Zerathe

J Carcaillet

R Vassallo

L Ravanel

L Tavernier

Jean-François Buoncristiani

J-L Mugnier

J- F Buoncristiani

Late Holocene initiation of a deep rock slope failure in an Alpine valley revealed by 10 Be surface exposure dating (Chamonix, France)

Keywords: Alpine glacial valley, Rock slope failure, Cosmogenic radionuclide dating, Natural hazard, Chamonix valley

Late Holocene initiation of a deep rock slope failure in

an alpine valley revealed by 10Be surface exposure dating (Chamonix, France)

Introduction

Shapes of alpine valleys have been deeply influenced by glacial cycles involving glacier retreats and advances over the Quaternary period [START_REF] Harbor | A numerical model of landform development by glacial erosion[END_REF][START_REF] Herman | Glacial hydrology and erosion patterns: A mechanism for carving glacial valleys[END_REF]. These processes have formed U-shaped valleys with steepening slopes that are rejuvenated by gravitational processes [START_REF] Le Roux | CRE dating on the head scarp of a major landslide (Séchilienne, French Alps), age constraints on Holocene kinematics[END_REF][START_REF] Crosta | Deep seated gravitational slope deformations in the European Alps[END_REF][START_REF] Riva | Damage-Based Time-Dependent Modeling of Paraglacial to Postglacial Progressive Failure of Large Rock Slopes[END_REF]. Glacial debuttressing and mechanical stress release have often been mentioned as causes that favor slope instabilities that can lead to large scale landslides or rock slope failures [START_REF] Holm | The landslide response of alpine basins to post-Little Ice Age glacial thinning and retreat in southwestern British Columbia[END_REF][START_REF] Bigot-Cormier | Chronological constraints on processes leading to large active landslides[END_REF][START_REF] Cossart | Slope instability in relation to glacial debuttressing in alpine areas (Upper Durance catchment, southeastern France): Evidence from field data and 10 Be cosmic ray exposure ages[END_REF][START_REF] Ballantyne | Rock-slope failure following Late Pleistocene deglaciation on tectonically stable mountainous terrain[END_REF]. Predisposing factors, such as lithology, rock mass characteristics, or geologic structures, play an important role in rock slope initiation and mountain slope evolution.

There are many potential triggering factors for gravitational structures in mountainous regions, sometimes working together, such as exceptional rainfall episodes [START_REF] Guzzetti | Landslides triggered by the 23 November 2000 rainfall event in the Imperia Province, Western Liguria, Italy[END_REF][START_REF] Pothérat | Néotectonique et grands mouvements de versant. Le cas de Séchilienne (Isère, France)[END_REF], seismic activity [START_REF] Chen | Large earthquake-triggered landslides and mountain belt erosion: The Tsaoling case, Taiwan[END_REF][START_REF] Janjou | Large rock avalanches triggered by the M 7.9 Denali Fault, Alaska, earthquake of[END_REF], changes in groundwater regime (Pisani et al., 2010), or permafrost degradation [START_REF] Ravanel | Climate influence on rockfalls in high-Alpine steep rockwalls: The north side of the Aiguilles de Chamonix (Mont Blanc massif) since the end of the 'Little Ice Age[END_REF]. All these factors control the spatial and temporal evolution of the instabilities, the landscape morphologies [START_REF] Crosta | Deep seated gravitational slope deformations in the European Alps[END_REF], and the risks these instabilities represent for human communities and infrastructures.

Rock slope failures (RSFs) such as rockslides, rock avalanches, or rockfalls, are common hazards in mountainous areas. Due to climate changes and thawing permafrost, the frequency and severity increase at high altitude and catastrophic events can occur (e.g. [START_REF] Ravanel | Climate influence on rockfalls in high-Alpine steep rockwalls: The north side of the Aiguilles de Chamonix (Mont Blanc massif) since the end of the 'Little Ice Age[END_REF].

Conversely, deep-seated gravitational slope deformations (DSGSD) or slow-moving rockslides generally do not seem to directly threaten humans. They evolve at slow displacement rates (several mm yr -1 ) over long periods and are characterized by specific geomorphological features such as counterscarps, double ridges, trenches, and tension cracks [START_REF] El Bedoui | Deep-seated failure propagation in a fractured rock slope over 10,000 years: The La Clapière slope, the south-eastern French Alps[END_REF][START_REF] Le Roux | CRE dating on the head scarp of a major landslide (Séchilienne, French Alps), age constraints on Holocene kinematics[END_REF]. DSGSDs may occur with a time lag relative to glacier disappearance [START_REF] Hormes | 10 Be exposure ages of a rock avalanche and a late glacial moraine in Alta Valtellina, Italian Alps[END_REF][START_REF] Ivy-Ochs | Surface exposure dating of the Flims landslide, Graubünden, Switzerland[END_REF]Prager et al., 2009b), supporting the hypothesis that even when the glaciers retreat they undergo progressive failure in subcritical stress conditions [START_REF] Eberhardt | Numerical analysis of initiation and progressive failure in natural rock slopes-The 1991 Randa rockslide[END_REF][START_REF] Brideau | The role of tectonic damage and brittle rock fracture in the development of large rock slope failures[END_REF][START_REF] Riva | Damage-Based Time-Dependent Modeling of Paraglacial to Postglacial Progressive Failure of Large Rock Slopes[END_REF]. Although they can appear stable or metastable, they can be reactivated and record sudden accelerations (see a review in [START_REF] Pánek | Temporal behavior of deep-seated gravitational slope deformations: A review[END_REF]. Understanding the long-term evolution and identifying the triggers of such structures are thus keys to estimating potential hazards in the near future.

Geochronological investigations allow the understanding of the temporal evolution of large RSFs (e.g. [START_REF] Pánek | Recent progress in landslide dating: a global overview[END_REF][START_REF] Pánek | Temporal behavior of deep-seated gravitational slope deformations: A review[END_REF]. In the last decade, cosmic ray exposure (CRE) dating method, such as with in-situ produced beryllium-10 ( 10 Be), has been widely applied either to boulders in ladslide debris deposits (e.g. [START_REF] Zeng | 10Be dating and seismic origin of Luanshibao rock avalanche in SE Tibetan Plateau and implications on Litang active fault[END_REF] or along headscarp walls (e.g. [START_REF] Böhme | Comparison of monitoring data with paleo-slip rates: Cosmogenic nuclide dating detects acceleration of a rockslide[END_REF]. For instance, these applications revealed successive failure phases of RSFs on single site but with time lag between each event reaching several thousand years (e.g. [START_REF] Delgado | Giant landslide triggerings and paleoprecipitations in the Central Western Andes: The aricota rockslide dam (South Peru)[END_REF], synchronicity of slope failures with past climate changes (e.g. Sepulveda et al., 2022), long-term progressive failure leading to landslide slipe rate increase (e.g. [START_REF] Schwartz | Cosmic ray exposure dating on the large landslide of Séchilienne (Western Alps): A synthesis to constrain slope evolution[END_REF], among others.

In this study, we investigated a large RSF recently identified in the Aiguilles Rouges massif (Chamonix valley, western European Alps, France). We used the 10 Be dating method to establish its long-term evolution. We also performed field mapping supported by aerial photograph analyses and the interpretation of the high-resolution light detection and ranging (LiDAR)-derived digital elevation model (DEM) to determine the geomorphology of the area and the relationship with RSF landforms. This paper has the following four aims: (i) to constrain the timing of the initiation of the RSF, (ii) to explore the link with local glacier extension, (iii) to identify the driving and triggering factors of the RSF, and (iv) to evaluate the potential threat it represents for the downslope population.

Settings

Geographic context

The Aiguilles Rouges massif (ARM) is located on the northwestern side of the Chamonix valley (western European Alps, France), west of the Mont Blanc massif (4808 m above sea level, a.s.l.) and is part of the alpine external crystalline massifs (Fig. 1). The ARM stretches over 15 km from northeast to southwest and goes from 1000 m a.s.l. near Chamonix to 2965 m a.s.l. at the Aiguille du Belvédère. It includes the 3279-ha Aiguilles Rouges Nature Reserve created in 1974. The Chamonix valley is highly populated and plays host to numerous touristic and economic activities including an important railway line and an international road linking France to Italy and Switzerland.

Our study site, called the Argentière crevasse (Fig. 1), is located in the Chéserys area, upslope of the Argentière village.

The massif is divided into three parts. The upper part of the Reserve is characterized by vertical walls and numerous remnant glaciers and alpine lakes. Then, the rhododendron moor marks the transition between the forest and alpine meadows. A dense spruce forest covers the steep slopes of the lower part of the Reserve. In terms of geomorphology, the landscapes of the upper slopes have mainly been shaped by glacial erosion and paraglacial processes, whereas screes and rockfall deposits from periglacial processes are major components of the lower slopes. Fallen rocks can sometimes reach more than 50 m across. One of these gravitational structures has been known since the 19 th century as the iconic Aiguillette d'Argentière (1893 m a.s.l.), a rock slice of approximately 25 m high separated from the rockwall (Fig. 2) On the landscape, trimlines (Fig. 1) indicate that the area was covered by ice during the Last Glacial Maximum (approximately 21 ka ago; [START_REF] Clark | The Last Glacial Maximum[END_REF] at approximately 2600 m a.s.l. [START_REF] Coutterand | Paléogéographie du dernier maximum glaciaire du Pléistocène récent de la région du Massif du Mont Blanc, France[END_REF]. Glacier thinning could have occurred very quickly after 16.6 ± 0.6 ka ago [START_REF] Lehmann | Postglacial erosion of bedrock surfaces and deglaciation timing: New insights from the Mont Blanc massif (western Alps)[END_REF]. Regional synthesis, mainly based on relative geomorphological analysis and relative chronology, suggest that the Col des Montets (1461 m a.s.l.) was free of ice before the Younger Dryas [START_REF] Jaillet | La transition Tardiglaciaire/Holocène à travers les fluctuations du glacier du Tour (Vallée de Chamonix, Alpes du Nord françaises) [Lateglacial/Holocene transition through glacier du Tour fluctuations (upper Chamonix valley, French Alps)[END_REF]. Using 10 Be cosmogenic dating on the frontal moraine of the Argentière glacier confirms that the glacier was restricted to the bottom of Chamonix valley at ca. 12 ka ago [START_REF] Protin | Climatic reconstruction for the Younger Dryas/Early Holocene transition and the Little Ice Age based on paleo-extents of Argentière glacier (French Alps)[END_REF] and lost its downslope continuity.

On the southern slopes of the ARM, small cirque glaciers were nonetheless present until the Younger Dryas [START_REF] Nicoud | Arrêt 4 -L'ombilic de Chamonix-Les Houches (Les Bossons). Collection EDYTEM[END_REF] and even to approximately 8 ka ago as shown by 10 Be cosmogenic dating of glacial polish [START_REF] Cara | The 1905 Chamonix earthquakes: Active tectonics in the Mont Blanc and Aiguilles Rouges massifs[END_REF]. Numerous small lakes and bogs have set up in the glacial overdeepening. Furthermore, rock glaciers and local moraines suggest that small glaciers developed during the Little Ice Age in the upper part of the two sides of the ARM. It is still glacierized above 2400 m a.s.l. with five small glaciers (< 0.1 km 2 ), and locally permafrost-affected rockwalls [START_REF] Magnin | Modélisation statistique de la distribution du permafrost de paroi: Application au massif du Mont Blanc. Géomorphologie : Relief, Processus[END_REF]. Figure 3 shows the entire study area seen from the Glacier de Lognan, on the other side of the valley.

Geology and tectonics:

The ARM is mostly composed of metamorphic rocks such as gneiss, mica schist, and granite [START_REF] Von Raumer | Les massifs du Mont Blanc et des Aiguilles Rouges: Témoins de la formation de la croûte varisque dans les Alpes occidentales[END_REF] and is crossed by the active Vallorcine fault (Fig. 1). This fault is one of the major tectonic structures of the area. It extends between the Rhône River and Chamonix and regularly reactivates. The Vallorcine fault is the geological contact between the Aiguilles Rouges gneiss and the Vallorcine granite. It was presumably the seismic location of the Mw 5.3 earthquake that affected the area in 1905 [START_REF] Cara | The 1905 Chamonix earthquakes: Active tectonics in the Mont Blanc and Aiguilles Rouges massifs[END_REF] and the Vallorcine seismic swarm is running along the fault [START_REF] Ritz | New perspectives in studying active faults in metropolitan France: the "Active faults France" (FACT/ATS) research axis from the Resif-Epos consortium[END_REF].

The Remuaz fault (Fig. 4a) extends from the southern end of the Vallorcine fault for approximately 15 km between the Col des Montets and the Chéserys area. It is an active fault with a dominant dextral strike-slip component compatible with a tectonic origin for the postglacial deformation as suggested by shifted glacial landforms [START_REF] Ritz | New perspectives in studying active faults in metropolitan France: the "Active faults France" (FACT/ATS) research axis from the Resif-Epos consortium[END_REF]. The southern part of the Remuaz fault is oblique to the Arve valley. The southwestern end disappears under a scree on the steep slopes and possibly under sedimentary rocks in the Chamonix valley.

Several gravitational scarps have been mapped by the Bureau de Recherches Géologiques et Minières (BRGM; Pairis et al., 1992) and some have been interpreted as deep-seated RSFs by [START_REF] Blondeau | Ruptures de Versant Rocheux (RVR) à l'échelle des Alpes occidentales: Inventaire systématique, analyse spatiale, perspectives patrimoniales[END_REF] and [START_REF] Blondeau | Rock slope failure in the Western Alps: A first comprehensive inventory and spatial analysis[END_REF]. Furthermore, displacement rates between 2014and 2018 were estimated over the western Alps using interferometric synthetic aperture radar (InSAR) time series from 130 Sentinel-1 acquisitions and 800 interferograms [START_REF] André | Four years of InSAR time series analysis reveals an unprecedent inventory of active DSGSD in the Western Alps[END_REF]. These satellite data suggest a present-day downhill creeping in some portions of the steep slope of the ARM, but more work is needed to precisely estimate a motion value. Nonetheless, there has never been a quantitative study about the geometry of the geological objects and rates of the gravitational processes, which is the purpose of this work. To avoid the complexity related to the spatial superposition of active tectonic and gravitational features, we focus on the gravitational features located downslope of the clearly expressed part of the Remuaz fault (Fig. 4a). Our study site is located within the gneiss between the slightly flat area and the valley slope.

Methods

Geomorphologic and structural methods

The general strategy is based on three methods as follows: 1) A cartographic approach using aerial images acquired and orthorectified by Institut national de l'information géographique et forestière (IGN) with a resolution of 0.2 m and a high-resolution DEM from the Institut National de la Recherche Agronomique (INRAe) made by airborne LiDAR in 2012 with meter-scale resolution. 2) A visual reconnaissance from a helicopter that also allowed the acquisition of high-resolution photos that were used to make a local photogrammetric three-dimensional (3D) model using Agisoft LLC Metashape software (professional edition, version 1.8) and reached a resolution < 0.5 m (see the result here: [START_REF] Buoncristiani | Rémuaz [Sketchfab[END_REF]. Cross sections derived from this DEM allowed the precise determination of gravitational structures' geometries. 3) Field work to determine structural measurements (orientation and dip of the fractures as well as amplitude of displacement along the fractures). For open fractures (called crevasses hereafter), we chose to measure the total displacement vector from characteristic irregularities located both at the footwall and at the hanging wall and to define this vector by its norm (total displacement measured with a laser range finder), its orientation with respect to north in the horizontal plane, and its dip (Fig. 5). The total displacement is composed of two distinct vertical and horizontal components. If the dip is greater than 45°, the magnitude of the vertical component is greater than that of the horizontal component. Furthermore, the upslope wall of the crevasse forms a clear morphological scarp if the dip of the displacement vector is greater than the dip of the topographic slope (). The crevasse's strike and dip () are mean values estimated from aerial photographs and field measurements. The opening is estimated in a horizontal plane perpendicular to the crevasse orientation.

Cosmogenic dating

To constrain the timing of both glacier retreat and initiation of the RSF, we performed CRE dating using in-situ beryllium-10 ( 10 Be). In the southern part of the ARM, the dominant lithology favored the use of 10 Be in quartz. Sampling locations are given in Figure 4a-d and the characteristics of the sampled surfaces are given in Table 1. In total, 11 rock samples of approximately 0.5 kg to 1 kg were extracted. Two types of morphologies were sampled: a near-vertical scarp of gravitational origin (9 samples) and glacially polished surface (2 samples). The two glacially polished surfaces are located at the hanging wall (AIG20-1, top of the scarp) and the footwall of the sampled gravitational scarp (AIG20-7).

Samples were prepared at the geo-thermo-chronology (GTC) platform [Institut des Sciences de la Terre (ISTerre) lab, Grenoble, France] following a procedure adapted from [START_REF] Brown | Examination of surface exposure ages of Antarctic moraines using in situ produced 10 Be and 26 A[END_REF] and [START_REF] Merchel | An Update on Radiochemical Separation Techniques for the Determination of Long-Lived Radionuclides via Accelerator Mass Spectrometry[END_REF]; see Appendix A. Then, 10 Be/ 9 Be ratios were measured at the Accelerator Mass Spectrometry facility (ASTER), CEREGE, Aix-en-Provence, France (see Appendix A and references therein).

Exposure ages were calculated using the online CREp calculator [START_REF] Martin | The CREp program and the ICE-D production rate calibration database: A fully parameterizable and updated online tool to compute cosmic-ray exposure ages[END_REF]. We used a global averaged time-dependent sea level high-latitude (SLHL) 10 Be production rate of 4.09 ± 0.19 atoms g -1 yr -1 [START_REF] Martin | The CREp program and the ICE-D production rate calibration database: A fully parameterizable and updated online tool to compute cosmic-ray exposure ages[END_REF]. As most of the samples are from a south-exposed slope steeper than 45°, preventing the persistence of snow, ages were computed without considering snow shielding and assuming a negligible denudation. Results are presented in Table 3.

Exposure ages of samples extracted along the gravitational scarp should be considered as the maximum apparent ages because a significant inheritance may have been accumulated at depth before direct scarp exposure (e.g., [START_REF] Zerathe | Dating chert (diagenetic silica) using in-situ produced 10 Be: Possible complications revealed through a comparison with 36 Cl applied to coexisting limestone[END_REF][START_REF] Zerathe | Mid-Holocene cluster of large-scale landslides revealed in the Southwestern Alps by 36 Cl dating. Insight on an Alpine-scale landslide activity[END_REF]. Indeed, an inherited component of cosmogenic radionuclide concentration acquired before direct exposure should be considered for surface samples as well as at depth (e.g., [START_REF] Benavente | Active transpressional tectonics in the Andean forearc of southern Peru quantified by 10 Be surface exposure dating of an active fault scarp: Active Tectonics in Southern Peru[END_REF][START_REF] Schwartz | Cosmic ray exposure dating on the large landslide of Séchilienne (Western Alps): A synthesis to constrain slope evolution[END_REF][START_REF] Hilger | How significant is inheritance when dating rockslide boulders with terrestrial cosmogenic nuclide dating?-A case study of an historic event[END_REF][START_REF] Břežný | Old but still active: > 18 ka history of rock slope failures affecting a flysch anticline[END_REF]. In the present case, the sampled gravitational scarp affects a glacially polished surface (Fig. 5) leading to a basic set of three hypotheses for inheritance calculation. 1) At the time of the glacier retreat, the polished surface is free of cosmogenic nuclides due to the previous strong ice abrasion. 2) Cosmogenic production starts on polished surface through direct exposure (t0), as well as at depth due to the penetration of high-energy particles (neutrons and then muons; [START_REF] Braucher | Determination of muon attenuation lengths in depth profiles from in situ produced cosmogenic nuclides[END_REF]. A large quantity of inherited nuclides can form before scarp exposure if the slip event is significantly delayed with respect to glacier retreat. 3) Finally, when the slip starts (t1), the scarp is directly exposed to cosmic rays and accumulates an additional quantity of cosmogenic nuclides. Deciphering the different steps and the 10 Be accumulation component is based on glacier retreat timing (t0) deduced from surface samples. An inheritance can be calculated using the classic equation (see Appendix B) used for calculation of in-situ cosmogenic nuclide accumulation [START_REF] Lal | Cosmic ray labeling of erosion surfaces: In situ nuclide production rates and erosion models[END_REF] as described by [START_REF] Břežný | Old but still active: > 18 ka history of rock slope failures affecting a flysch anticline[END_REF] and considering an integration time tint = t0 -t1 and an estimated depth, determined as follows:

𝑍 𝐸 = sin 𝛼 × 𝐿 sin 𝜃 , ( 1
)
where  is the dip of the topographic slope, L the distance between the sample and the top of the scarp, and  is the dip of the crevasse. Finally, this inherited 10 Be concentration is substracted from the measured concentration (Table 3). Corrected ages are calculated as previously described using the same parameters on the online CREp calculator [START_REF] Martin | The CREp program and the ICE-D production rate calibration database: A fully parameterizable and updated online tool to compute cosmic-ray exposure ages[END_REF].

Results

Geomorphology and structural geology

The tectonic structures are characterized by a rather planar pattern (Fig. 4a) and the Remuaz fault is a subvertical fault dipping strongly to the southeast. A second fault, which we call the Aiguillette fault, is trending N52° and dipping 79° to the northwest.Downslope of the Remuaz fault, we have identified a rock crevasse that is several kilometers long (Fig. 4a), extending between 1700 and 2000 m a.s.l. The walls of the crevasse are very irregular and formed by an amalgam of different fractures. In addition, some portions of these crevasses are covered by scree associated with snow avalanches and boulder/rock falls from the upper cliffs (Fig. 4a-c).

In the northeastern part of the Argentière crevasse (Fig. 4c), downslope of the Tête aux Vents, the rupture occurred upslope of a cliff where the slope changes from 78° to 35° (Fig. 5a). The vertical component that moved down the downslope wall reaches 7 m (Fig. 6a) and is greater than the horizontal component, as the dip of the displacement is always greater than 45° (Table 2). The total displacement between the two walls reaches 13 m and is nearly parallel to the approximately N100° main dip of the hillslope. As the crevasses are composed of several segments with different orientations, the distances measured perpendicular to the walls and strike-slip components are variable. The opening of the rock crevasse reaches 10 m where the crevasse is oriented N42°E perpendicular to the displacement, whereas the opening is only 1.5 m where the crevasse is oriented N164° and associated with a large strike-slip component. The cliff present in the prolongation of the northeastern termination of the Argentière crevasse (Fig. 5a) probably corresponds with the footwall of a now collapsed gravitational structure. Indeed, a large scree including a chaotic array of blockssome of which reach sizes greater than 10 3 m 3 (Fig. 5f)-has been fed by collapses from this area.

The southwestern part of the rock crevasses (Fig. 4b), located close to the Chéserys chalet, shows a maximum displacement of 7.2 m (Fig. 5e) and the magnitude of the vertical component is less than that of the horizontal component, with the dip of the displacement vector less than 45° (Table 2), and the dip of the displacement is close to the topographic slope. At the southwestern end, the motion of the crevasse system is transferred toward the north by a fracture that is nearly parallel to the displacement vector and crosses the Remuaz fault (Fig. 4a). This transfer fault connects to a system of several fractures on the western side of the Remuaz fault that are nearly parallel to it.

Chronological results

We sampled the crevasse in the portion where the opening width is the smallest and is similar to a fault scarp (site 1, Table 2). This is situated where the angle between the motion vector and the fracture location is the smallest, 12° (Fig. 5c,d). 10 Be concentrations and related exposure ages are reported in Table 3. In the following section, ages are considered with external uncertainties including the uncertainty on the production rate. Exposure ages obtained from the offset surface are 17.5 ± 1.0 ka (AIG20-1) and 18.9 ± 1.0 ka (AIG20-7), for the hanging wall scarp and its footwall, respectively. These two values are indistinguishable within uncertainties and have a weighted mean of 18.2 ± 0.7 ka. This confirms the geomorphological observation that the gravitational scarp cut a glacially polished surface that is of the same age and that was initially continuous along the slope.

The raw exposure ages (i.e., calculated without inheritance correction) obtained along the scarp, from the scarp top to the scarp toe, range from 3.0 ± 0.6 ka (AIG20-2) to 0.7 ± 0.3 ka ago (AIG20-6)

for profile P1 and from 2.3 ± 0.4 ka (AIG20-17) to 1.0 ± 0.3 ka (AIG20-19) for profile P2 (Fig. 6). These ages should be considered as maximum exposure ages. Figure 7 shows that the age distributions of the two profiles are almost identical and that the exposure ages decrease as a function of the distance to the top scarp, except for the first sample of profile P2 (AIG20-16: 1.6 ± 0.3 ka) which is considered as outlier due to scarp rejuvenation and that is thus discarded for the following discussion. With the inheritance correction (see section 3.2), the exposure ages become much younger, ranging from 1.3 ± 0.3 to 0.6 ± 0.3 ka and from 2.0 ± 0.3 to 0.9 ± 0.3 ka, for profiles P1 and P2, respectively (Table 3). The corrected ages should be considered as minimum exposure ages.

DISCUSSION

A complex fracture pattern above the village of Argentière

This new structural work has clarified the geometry of the deep-seated gravitational slope deformation system located above the village of Argentière. The crevasse system does not have the same direction as the plane of the Remuaz fault (Fig. 4) and the similarly oriented Aiguillette fault dips to the northwest while the crevasse system dips to the southeast (Fig. 6). At the southwestern end, the crevasse system intersects the Remuaz fault. The amount and the dip of the displacement increase from southwest to northeast (Table 2). Currently, at the northeastern end of the Argentière crevasse, the cliff corresponds to the extension of the footwall of the crevasse that has probably been exposed by the collapse of a former block (Fig. 4c,5a). This portion of the cliff suggests that the surface rupture does not propagate far below the surface but tends to emerge at the base of the cliff. These observations indicate a more mature structure in the NE, and suggest a propagation of the structure to the southwest. This would suggest that the structure initiated later toward the southwest than in the portion where the chronological constraints were obtained. All these geometric characteristics suggest that the geometry of the gravity system is largely independent of the geometry of the Remuaz fault and no evidence exists to relate the crevasse development with the active fault movements.

Northeastern Argentière crevasse initiation and kinematics

The exposure ages corrected with inheritance data provide estimates for the timing of the crevasse initiation. Slip along the scarp could have occurred progressively, as corrected ages (Fig. 7b) are generally younger with depth. The standard deviations of several linear regression fits are in the order of measurement uncertainty. The least-squares regression method suggests an average slip rate of 7.6 m/ka, with standard deviations between data and linear fits that reach 0.38 ka. On the other hand, as most of the corrected exposure ages are compatible within uncertainties (Fig. 7c), an instantaneous (or very fast) event could have also occurred at a time equal to the weighted mean of 1.3 ± 0.6 ka. An instantaneous slip at 1.3 ka provides a slightly greater standard deviation of 0.45 ka.

The instantaneous slip model at 1.3 ka provides a lower bound for the initiation of the crevasse.

The end of the motion would have occurred more than 0.6 ka ago based on the lowest slip rate model. This agrees with the samples located at the base of the scarp (AIG20-6 and AIG20-19) that indicate corrected exposure ages of 0.6 ± 0.3 ka and 0.9 ± 0.3 ka ago. In summary, the kinematic activity of the main crevasse would have started less than 1.3-2.5 ka ago. Furthermore, it could have been progressive and it cannot be excluded that movement is still ongoing on the main crevasse and the other crevasses located downslope. This possible present-day activity is consistent with the 2014-2018 InSAR displacement fields preliminarily estimated at a few millimeters per year below the northeastern end of the Argentière crevasse [START_REF] André | Four years of InSAR time series analysis reveals an unprecedent inventory of active DSGSD in the Western Alps[END_REF].

Time lag between gravitational failure initiation and glacier retreat

A time lag between gravitational collapse and glacier retreat has frequently been observed elsewhere, and the fundamental role of postglacial debuttressing in the initiation of gravitational structures on alpine valley flanks has been identified [START_REF] Cossart | Slope instability in relation to glacial debuttressing in alpine areas (Upper Durance catchment, southeastern France): Evidence from field data and 10 Be cosmic ray exposure ages[END_REF][START_REF] Mccoll | Paraglacial rock-slope stability[END_REF]. However, information to support this relation based on dating both the initiation of the gravity structure and the time of debuttressing of the slopes remains scarce. In the western Alps, the Tinée valley was completely deglaciated approximately ca. 13 ka ago [START_REF] Bigot-Cormier | Chronological constraints on processes leading to large active landslides[END_REF] while the first gravitational movement of the La Clapière landslide occurred at approximately 10 ka, implying a time lag of approximately 3 ka [START_REF] Bigot-Cormier | Chronological constraints on processes leading to large active landslides[END_REF][START_REF] El Bedoui | Deep-seated failure propagation in a fractured rock slope over 10,000 years: The La Clapière slope, the south-eastern French Alps[END_REF][START_REF] Jomard | Morphological analysis of deep-seated gravitational slope deformation (DSGSD) in the western part of the Argentera massif. A morpho-tectonic control?[END_REF]. In the Romanche Valley (France), Le Roux et al. ( 2009) and [START_REF] Schwartz | Cosmic ray exposure dating on the large landslide of Séchilienne (Western Alps): A synthesis to constrain slope evolution[END_REF] found that glacier retreat at the summit scarp was achieved between ca. 17 and 13 ka ago while the landslide initiated between 8 and 6 ka ago, implying that the rupture occurred with a delay of minimum 5 ka. At Flims (Switzerland), a rockslide occurred more than 2.5 ka after deglaciation (Ivy-Ochs, 2009). In the Val Viola (Italy), the rockslide occurred more than 4 ka after deglaciation [START_REF] Hormes | 10 Be exposure ages of a rock avalanche and a late glacial moraine in Alta Valtellina, Italian Alps[END_REF]. The Fernpass (Austria) rockslide occurred 1 ka after the retreat of the glacier (Prager et al., 2009a). These studies indicate that deglaciation occurred earlier than the rockslide initiation but in most cases the delay is less than 5 ka. In the present study, the time lag between the initiation of the dated scarp and the disappearance of the glacier at the level of the scarp is significantly higher, with a value reaching approximately 15 ka.

Delayed deformation models involving both debuttressing and fluid pressure

Glacial processes can be a cause of RSF initiation, although they precede the initiation by several thousand years. Mechanical modeling [START_REF] Riva | Damage-Based Time-Dependent Modeling of Paraglacial to Postglacial Progressive Failure of Large Rock Slopes[END_REF] indicates that large alpine rock slopes undergo long-term evolution in paraglacial to postglacial environments. Progressive RSFs of deglaciated slopes occur due to rock mass weakening, increased permeability, and damage linked to fluid-to-solid hydromechanical coupling. These progressive failures promote the development of potentially catastrophic rockslides that would occur several thousands to ten thousand years after the debuttressing [START_REF] Riva | Damage-Based Time-Dependent Modeling of Paraglacial to Postglacial Progressive Failure of Large Rock Slopes[END_REF]. The large time lag between initiation and gravitational activity raises the possibility of rainfall, snowmelt, or extreme meteorological events as main triggers of the gravitational activities. [START_REF] Ivy-Ochs | Surface exposure dating of the Flims landslide, Graubünden, Switzerland[END_REF] found that some of the largest landslides in the Alps did not occur directly after deglaciation but during the Early to Middle Holocene. They initiated close to the Preboreal/Boreal transition (approximately 10 ka ago). That is the case for Köfels (9.8 ka ago, [START_REF] Ivy-Ochs | The age of the Köfels event: Relative, 14 C and cosmogenic isotope dating of an early Holocene landslide in the Central Alps (Tyrol, Austria)[END_REF], Kandertal (9.6 ka ago, Tinner et al., 2005) and Flims (8.9 ka ago, [START_REF] Ivy-Ochs | Surface exposure dating of the Flims landslide, Graubünden, Switzerland[END_REF], for example. This transition is marked in Western Europe by a shift to significantly warmer and wetter conditions, with an increase of the mean temperature of 1°C to 2°C [START_REF] Rasmussen | A new Greenland ice core chronology for the last glacial termination[END_REF] and heavy annual precipitations, as evidenced by lake level [START_REF] Magny | Orbital imprint on Holocene palaeohydrological variations in west-central Europe as reflected by lake-level changes at Cerin (Jura Mountains, eastern France)[END_REF]. Therefore, external hydrological parameters may have an amplifying effect on the postdebuttressing gravitational instabilities. A synthesis of all large (>10 6 m 3 ) rock slope instability events has been carried out across the Alps, including the nonglaciated parts (Zerathe et al., 2014, and references therein). Two temporal clusters have been identified regardless of process (including initiation/reactivation and catastrophic collapse of Sackungen, deep-seated landslides, earthflows, and rockfalls). The first cluster, between 11 and 8 ka ago, is related to paraglacial readjustment by progressive failure at the transition to the Holocene (Prager et al., 2009a,b). The second, 5 -3 ka ago [START_REF] Zerathe | Mid-Holocene cluster of large-scale landslides revealed in the Southwestern Alps by 36 Cl dating. Insight on an Alpine-scale landslide activity[END_REF], is related to seismicity [START_REF] Prager | Age distribution of fossil landslides in the Tyrol (Austria) and its surrounding areas[END_REF] or paleohydrological cycles during the Middle Holocene climatic transition [START_REF] Prager | Age distribution of fossil landslides in the Tyrol (Austria) and its surrounding areas[END_REF][START_REF] Zerathe | Mid-Holocene cluster of large-scale landslides revealed in the Southwestern Alps by 36 Cl dating. Insight on an Alpine-scale landslide activity[END_REF].

The initiation of the Argentière crevasse occurred during the Late Holocene, later than all the previously mentioned gravitational instabilities, and this slope failure had likely required the superposition of several factors. Topography first plays a major role in the rock slope instability.

In the case of the Argentière crevasse (Fig. 8), the rupture occurred upslope of a rock wall with a slope of 78°. This topography is the result of the glacier erosion that shaped the slopes at the valley scale before the LGM. However, the very long time lag (ca. 15 ka) between the complete deglaciation, which rapidly occurred at Argentière, releasing the entire slope between 18 and 15 ka ago (Fig. 8), and the RSF initiation (1.3 -2.5 ka ago), reasonably exclude a direct effect of slope debuttressing here. However, it is not excluded that debuttressing could have led to previous rockfalls 15 ka ago along the valley of Chamonix as several blocks were observed along the lower slopes, but this topic would require a dedicated study. Eventually, wet climatic events, and particularly those beginning approximately 2.7 ka ago at the transition from sub-Boreal to sub-Atlantic periods (Fig. 9), may have favored the hydraulic loading of interconnected fractured areas during progressive mechanical damage. Then, a drift toward a peak of wetter conditions that is evidenced around 1.6 ka ago [START_REF] Arnaud | Lake Bourget regional erosion patterns reconstruction reveals Holocene NW European Alps soil evolution and paleohydrology[END_REF], as recorded in the sedimentary archives of the Bourget lake (Western Alps) (Fig. 9), may also have accelerated the failure.

Regarding the permafrost, there is no observable evidence that ice is currently present in the Argentière crevasse, and [START_REF] Boeckli | Permafrost distribution in the European Alps: Calculation and evaluation of an index map and summary statistics[END_REF] did not model permafrost at the same altitude (1900 m a.s.l.) in their Alpine Permafrost Index Map. Furthermore, [START_REF] Heiri | Stacking of discontinuous regional palaeoclimate records: Chironomid-based summer temperatures from the Alpine region[END_REF] who used biotic proxies in lake sediments from French and Swiss Alps to reconstruct palaeotemperatures (15000-90 Cal BP),

shown that mid-Holocene July air temperatures generally range between 16.5 and 17.5°C [START_REF] Heiri | Stacking of discontinuous regional palaeoclimate records: Chironomid-based summer temperatures from the Alpine region[END_REF]. Records for the Anterne Lake (2000 m a.s.l), located less than 10 km to the northwest of the crevasse as the crow flies, show temperatures ranging from 7.5 to 11°C for the past 2 ka. [START_REF] Affolter | Central Europe temperature constrained by speleothem fluid inclusion water isotopes over the past 14,000 years[END_REF], thanks to Milandre Cave fluid inclusion temperature record, shown that temperature anomaly for central Europe was about -5°C 12000 years ago. These elements suggest that permafrost can be excluded at the altitude of our study site. Further studies would be necessary to determine the role played by seismicity in the initiation of the Argentière crevasse.

Gravitational hazard

The gravitational structure of the Argentière crevasse constitutes a possible significant hazard for the downslope village of Argentière, as well as road and rail infrastructures. In the documents concerning the risks to the town of Chamonix-Mont-Blanc, this structure has been identified. It has been noted that the Béchar spur constitutes a natural bund protecting the valley downslope of the northeastern Argentière crevasse (Plan de Prévention des Risques Naturels prévisibles, 2000). Modeling using Rapid mass movement simulation (RAMMS) software [START_REF] Tavernier | Etude structurale et trajectographique de la rupture de versant rocheux des Cheserys[END_REF] confirms that the village of Argentière is protected by the Béchar spur for blocks < 100 m 3 . [START_REF] Tavernier | Etude structurale et trajectographique de la rupture de versant rocheux des Cheserys[END_REF] shows that a very small percentage (less than 0.3%) of modeled rocks passes the Béchar spur and reaches the first infrastructures in the valley. However, further southwest, the valley is directly exposed to possible collapses as almost 40% of the modeled rocks cross the railway line [START_REF] Tavernier | Etude structurale et trajectographique de la rupture de versant rocheux des Cheserys[END_REF]. Eventually, the Argentière crevasse raises questions about its cohesion and its resistance to possible seismic shocks. Two scenarios are possible: either the different units that compose it fragment and form unitary blocks, or they fall apart in a single unit. On the one hand, the mobilized volume is of a hundred cubic meter, on the other hand, several tens of thousands. If unitary blocks can be stopped by the Béchar, the collapse of blocks with greater volume (5 ± 3 × 10 6 m 3 ) would threaten the valley and its inhabitants.

Conclusion

This Institut national de l'information géographique et forestière, IGN). Altitudes are in m a.s.l. Faults and geology are based on the Bureau de Recherches Géologiques et Minières (BRGM) mapping (Pairis et al., 1992a;Pairis et al., 1992b[START_REF] Janjou | Large rock avalanches triggered by the M 7.9 Denali Fault, Alaska, earthquake of[END_REF]. Northings and eastings are according to RGF93/Lambert-93 grids. Black dashed lines represent Last Glacial Maximum glacial upper limits [START_REF] Coutterand | Paléogéographie du dernier maximum glaciaire du Pléistocène récent de la région du Massif du Mont Blanc, France[END_REF][START_REF] Coutterand | Étude géomophologique des flux glaciaires dans les Alpes nord-occidentales au Pléistocène récent[END_REF]. Glacier extents are from the Randolph Glacier Inventory (RGI Consortium, 2017) 6.0.

Figure captions

Table captions

Table 1. Sample characteristics. T is the sample thickness; Z is the distance of the sample from the top of the scarp; ZE corresponds to the estimated depth before the scarp was exhumed; S is the shielding; and MQuartz is the quartz mass.

Table 2. Structural characteristics of the main crevasses from northeast to northwest (site locations shown in Fig. 4).

Table 3. 10 Be concentrations, calculated and corrected ages considering inheritance. Internal uncertainty corresponds to the 1 uncertainty without production rate errors. The in-house standard [START_REF] Braucher | Preparation of ASTER in-house 10 Be/ 9 Be standard solutions[END_REF] and accelerator mass spectrometer internal error (0.5%; [START_REF] Arnold | The French accelerator mass spectrometry facility ASTER: Improved performance and developments[END_REF] have been included. A blank value with a 10 Be/ 9 Be ratio of 6.95 × 10 -15 was used to correct the samples.

  These fractures are less planar than the Remuaz fault and their downslope compartments are lowered. Numerous fractures, parallel or perpendicular to the major crevasse, are observed. Many of them are slightly open (< 1 m). The vertical motion on these open fractures is very limited (< few centimeters). The use of a sounding line (thin rope with a weight) has shown depths greater than 15 m. The total rock volume involved is thus estimated at 5 ± 3 × 10 6 m 3 . The northeastern termination of the Argentière crevasse is currently unstable and a diachronic analysis, based on IGN aerial images, shows that an approximately 2.5 × 10 3 m 3 block collapsed sometime between 1949 and 1960.

  work describes initial research on a deep rock slope failure in the Aiguilles Rouges Nature Reserve located in the upper Chamonix valley, French Alps. It shows that a large crevasse extends for approximately 1 km in the gneiss above the village of Argentière. The total displacement, as well as the ratio of vertical to horizontal displacement component, decreases from northeast to southwest, suggesting a southwestward propagation. The rock mass located downslope of the Argentière crevasse is itself affected by numerous open fractures. The 10 Be dates of the escarpment at its northeastern termination suggest that the main development initiated at 1.3-2.5 ka ago and may still be ongoing. It should be stressed that the main development of this structure occurred well after the deglaciation of this area, which occurred about 18 ka ago. Other case studies focused on similar gravitational processes in the Alps demonstrated that the time lag between the initiation of RSFs and glacier retreat was in most cases less than 5 ka. Our study suggests a time lag reaching approximately 15 ka, which reasonably allow to discard this effect here. Predisposing factors (lithology, rock mass characteristics, and geologic structures) and potential triggering factors (exceptional rainfall events, seismic activity, changes in groundwater regime) have been discussed to understand the origins of the initiation of the Argentière crevasse. Its evolution is likely to constitute a future major risk for the upper part of the Chamonix valley. Further studies on the measurement of the current deformation, the stability of this zone, and the modeling of the trajectories of the rockfalls in this area are necessary.This study received financial support from the program "Investissements d'Avenir, Agence nationale de la recherche (ANR), labex Observatoire des sciences de l'univers de Grenoble (OSUG): habitability in a changing word." The authors thank Frédéric Berger and Jean-Matthieu Monnet (Institut national de la recherche agronomique, INRAe) for sharing the high-resolution Arve valley digital elevation model, the ASTER team (Georges Aumaître, Regis Braucher, Vincent Godard, Karim Keddaouche, CEREGE) for measuring the 10 Be concentrations. Two anonymous reviewers are acknowledged for their comments that helped to improve the manuscript.

Figure 1 .

 1 Figure 1. Geological context of the studied area with shaded 75 m Digital Elevation Model (source:

Figure 2 .

 2 Figure 2. Photo of the Aiguillette d'Argentière, famous rock-climbing site.

Figure 3 .

 3 Figure 3. Photograph of the entire mountain slope taken from the Glacier de Lognan, on the opposite

Figure 4

 4 Figure 4. a) Geomorphologic settings of the study area showing the main structural and gravitational

Figure 5

 5 Figure 5. a) Global view of the northeastern Argentière rock crevasse. b) Location of the glacially

Figure 7 .

 7 Figure 7. Plot of the 10 Be exposure ages as a function of the distance from the top of the scarp. a) All

Figure 8 .

 8 Figure 8. Section through the upper Arve valley illustrating the position of the eastern end of the

Figure 9 .

 9 Figure 9. Sedimentation rate and terrigenous contribution in the Lac du Bourget (from Arnaud et al.,

Figure1Figure2

  Figure1

Sample 10 Be 10 Be age ± external uncertainty Internal uncertainty Inherited 10 Be concentration Inheritance/ measured Corrected concentration Corrected age ± external uncertainty

  TableClick here to access/download;Table;Table2_L_Courtial_Manent.docx

											Internal
											uncertainty
											(1)
		x10 4 at.g -1	(ka)	(ka)	(at.g -1 )	(%)		(at.g -1 )	(ka)	(ka)
	AIG20-1	19.30 ± 0.74	17.51 ± 1.01	0.65	-	-		-	-	-
	AIG20-2	Displacement 3.14 ± 0.60 3.01 ± 0.59	Strike of 0.57	Dip of displacement 1,80E+04	Mean crevasse 57 1,35E+04 Mean dip of the 1.26 ± 0.30	Mean opening 0.29
	AIG20-3	vector (norm) 1.94 ± 0.39 1.84 ± 0.38 displacement vector 0.37	vector 3,13E+03	16	orientation 1,62E+04	crevasse (a) 1.53 ± 0.33	of the crevasse 0.31
	AIG20-4 AIG20-5	1 1.86 ± 0.58 12 m 2 13 m 1.04 ± 0.48	1.76 ± 0.57 1.01 ± 0.46	N 100° 0.56 N 130 ° 0.45	65°E 1,31E+03 50°E 1,31E+03	7 13	N164°E 1,73E+04 N42°E 9,07E+03	72°E 1.64 ± 0.52 Vertical 0.88 ± 0.40	0.52 1.5 m 10 m 0.40
	AIG20-6	3 0.69 ± 0.32 7.2 m	0.66 ± 0.30	N 102° 0.30	40°E 5,86E+02	6	N8°E	6,32E+03	65°E 0.61 ± 0.27	3.7 m 0.29
		4	3.7 m		N 115°	45°E		N22°E		67°E	2 m
	AIG20-7	23.62 ± 0.81	18.87 ± 1.03	0.62	-	-		-	-	-
	AIG20-16	1.65 ± 0.35	1.56 ± 0.34	0.34	1,13E+04	68		5,23E+03	0.48 ± 0.11	0.11
	AIG20-17	2.37 ± 0.36	2.25 ± 0.36	0.35	2,44E+03	10		2,12E+04	2.01 ± 0.33	0.32
	AIG20-18	1.65 ± 0.36	1.56 ± 0.35	0.34	8,17E+02	5		1,56E+04	1.47 ± 0.33	0.32
	AIG20-19	1.00 ± 0.31	0.95 ± 0.29	0.29	5,37E+02	5		9,49E+03	0.89 ± 0.28	0.27

Acknowledgments

Data availability

Dataset related to this article (photography and precise description of the 10 Be sites, threedimensional (3D) model, etc.) can be asked to the corresponding author.

Author contributions

L. Courtial-Manent performed her Master's thesis on the subject, analyzed the data and wrote the paper, J.L. Mugnier led the project, worked on the tectonics aspects and wrote the paper. S. Zerathe worked on the age interpretation, the geomorphology mapping and wrote the paper, J. Carcaillet checked the quality of the geochemical processes and worked on the 10 Be concentrations. R. Vassallo, J.F. Buoncristiani and L. Ravanel have helped in the mapping process and in reviewing the manuscript. L. Tavernier did his first year of Master's thesis on this subject and he participated in the fieldwork to measure the displacement vectors.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Site


D Heller

M Rizk

R Douguet

A Baghdadi

J-Ph Diguet

Marine Objects Detection Using Deep Learning on Embedded Edge Devices

Keywords: Marine, Deep learning, Embedded edge devices, YOLO, Ship detection, Ship dataset, Optimization

Artificial Intelligence techniques based on convolution neural networks (CNNs) are now dominant in the field of object detection and classification. The deployment of CNNs on embedded edge devices targeting real-time inference sets a challenge due to the limited computing resources and power budgets. Several optimization techniques such as pruning, quantization and use of light neural networks enable the realtime inference but at the cost of precision degradation. However, using efficient approaches to apply the optimization techniques at training and inference stages enable high inference speed with limited degradation of detection performance. In this paper, we revisit the problem of detecting and classifying maritime objects. We investigate different versions of the You Only Look Once (YOLO), a state-of-the-art deep neural network, for real-time object detection and compare their performance for the specific application of detecting maritime objects. The trained YOLO networks are efficiently optimized targeting three recent edge devices: Nvidia Jetson Xavier AGX, AMD-Xilinx Kria KV260 Vision AI Kit, and Movidius Myriad X VPU. The proposed deployments demonstrate promising results with an inference speed of 90 FPS and a limited degradation of 2.4% in mean average precision.

I. INTRODUCTION

Marine object detection and classification are two essential tasks for many applications such as vessel identification and positioning, collision avoidance system, safe autonomous navigation, search and rescue mission, etc. Marine objects can span from stationary floating objects such as buoys to small boats and kayaks and other large vessels such as ferries, passenger ships and cargo ships. Surveillance adopting shared information from Electronic Chart Display and Information System (ECDIS) and Global Navigation Satellite System (GNSS) can provide locations of marine vessels [START_REF] Schöller | Assessing deep-learning methods for object detection at sea from LWIR images[END_REF]. However, this depends on the reliability of data, which may degrade due to spoofing, jamming or even dis-activating automatic identification system (AIS). Radar-based methods can be effective to detect the presence of large vessels. Small boats and floating objects on water surface are difficult to be identified [START_REF] Schöller | Assessing deep-learning methods for object detection at sea from LWIR images[END_REF].

Visual detection of marine objects using electro-optical sensors provides a solution for detecting and classifying marine objects [START_REF] Blanke | Outlook for navigation -comparing human performance with a robotic solution[END_REF]. Classification and detection of objects using captured images have been widely used in several application domains [START_REF] Prasad | Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey[END_REF] [START_REF] Bi | A survey of ship target feature extraction based on video surveillance[END_REF]. However, the characteristics of the scenes captured in marine environment arise additional challenges to the task of detecting objects in images or videos compared to other environments. Factors such as dynamic nature of the background, unavailability This work was supported in part by the Regional Council of Bretagne (through ODESSA and SURCOUF projects) and the City of Lorient (through SURCOUF project). of static cues, presence of small objects at distant backgrounds and illumination effects impact the performance of commonly used image processing and computer vision approaches [START_REF] Prasad | Challenges in video based object detection in maritime scenario using computer vision[END_REF]. Tides and waves lead to a continuously dynamic background in both spatial and temporal dimensions. Also, floating objects are subjected to a lot of motion with unpredictable patterns. The illumination of marine scenes varies due to weather conditions (haze, fog, rain, bright sunlight, twilight, etc.). Speckles and glints are mainly induced by the variation of the solar incident angle on water. Furthermore, the disparity of color gamut depends on illumination conditions. Color gamut varies respectively between dark, yellow and red, blue and gray during night, sunset, daylight, and hazy conditions. These factors affect the visibility of objects in marine environments and hence the detection performance. An effective technique for certain cases with specific illumination type, weather condition and water dynamicity may not suit other conditions.

Artificial intelligence (AI) techniques based on deep learning provide robust solutions to detect and locate objects. The achieved performance proves the relevance of convolution neural networks (CNNs) in circumventing existing computer vision challenges. Deep learning methods, known as deep neural networks, make use of multiple hidden layers between the input and output layers to learn a hierarchy of features that are invariant to geometric transformations from raw input images.

In this work, we investigate the use of deep neural networks to detect and classify marine objects. The state-of-the-art You Only Look Once (YOLO) networks are adopted to ensure high detection performance and high detection rate. Also, we make use of emergent optimization techniques to deploy the trained networks on embedded edge devices. The main contributions of this paper are:

• create a diverse dataset of marine objects, which has the widest number of classes and annotations compared to available datasets, • train and evaluate several YOLO neural networks with different sizes and architecture specifications, • apply structured pruning using sparsifying to reduce the network size while maintaining the detection performance, • optimize the trained networks towards implementation on popular edge devices to achieve the best compromise between inference speed and detection performance.

The rest of the paper is organized as follows. Section II presents a brief background and reviews related work. Section III describes the adopted methods for training and evaluation. Section IV illustrates the used structured pruning method. Section 979-8-3503-9851-9/22/$31.00 ©2022 IEEE V describes the deployment of models on edge devices and shows the obtained results. Finally, Section VI concludes the paper.

II. BACKGROUND AND RELATED WORK

A. Object detection methods

Previously, computer vision techniques based on feature extraction has been widely adopted to detect objects [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF] [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF]. Recently, AI methods with CNNs play dominant role in classifying and locating multi objects in images and videos leading to accurate detection. One-stage detection methods have been introduced to provide real-time performance with acceptable precision and accuracy. These methods exclude the stage of pre-selecting the regions of classification and abstract post-processing techniques (refining bounding boxes, eliminating duplicates and adjusting detection scores) used in two-stage methods such as the well known region-convolutional neural network (R-CNN) [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF] and its enhanced versions [START_REF] Girshick | Fast R-CNN[END_REF], [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF] in order to reduce the complexity and ensure real-time detection speed.

You Only Look Once (YOLO) has been recently proposed in [START_REF] Redmon | You Only Look Once: Unified, real-time object detection[END_REF] as an efficient one-stage CNN-based model that is able to detect multi objects in real-time. Published peer reviewed comparisons [START_REF] Qiao | Marine vision-based situational awareness using discriminative deep learning: A survey[END_REF][1] [START_REF] Zhang | Survey on deep learning-based marine object detection[END_REF] illustrate that YOLO outperforms twostage detection methods and other available one-stage methods such as single shot detector (SSD). Since introduced, many versions of YOLO have been introduced such as YOLOv2 [START_REF] Redmon | YOLO9000: Better, faster, stronger[END_REF], YOLOv3 [START_REF]YOLOv3: An incremental improvement[END_REF], YOLOv4 [START_REF] Bochkovskiy | YOLOv4: Optimal speed and accuracy of object detection[END_REF] and and YOLOv5. In YOLOv2 [START_REF] Redmon | YOLO9000: Better, faster, stronger[END_REF], the fully connected layers at the end have been eliminated and Darknet-19 architecture has been adopted. YOLOv3 [START_REF]YOLOv3: An incremental improvement[END_REF] uses Darknet-53 architecture and inherits the concept of residual networks. The detections are made at 3 different scales which enables the detection of small objects. YOLOv4 and YOLOv4tiny proposed initially in 2020 [START_REF] Bochkovskiy | YOLOv4: Optimal speed and accuracy of object detection[END_REF] optimize and improve every part of YOLOv3. The main optimization is to use CSPDarknet-53 as its backbone network for extracting features. The difference between YOLOv4-tiny and YOLOv4 is that the tiny version only has two YOLO heads at the end (2 scale factor instead of 3).

The experiments targeting Microsoft Common object in context (COCO) dataset [START_REF]COCO -common objects in context web site[END_REF] show that YOLOv4 is faster and more accurate than real-time neural networks EfficientDet [START_REF] Tan | Efficientdet: Scalable and efficient object detection[END_REF] and RetinaNet [START_REF] Lin | Focal loss for dense object detection[END_REF] provided by Google and Facebook respectively. Comparisons have been made between YOLOv3, YOLOv4 and YOLOv5, in which some authors claim that YOLOv4 is more accurate while others claim that YOLOv5 is more accurate. The reason for different reported results can be attributed to many factors, such as the different datasets used, the modified hyperparameters, etc [START_REF] Nepal | Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs[END_REF].

B. Maritime Detection Datasets

Object detection based on deep learning imposes the challenge of having sufficient dataset with object annotations for training and validation processes. Training using large datasets with diverse images results in robust networks and prevents the occurrence of overfitting or underfitting. In maritime context, only few datasets for maritime object detection are available publicly for research purposes. This subsection presents briefly these available published datasets.

VAIS dataset [START_REF] Zhang | Vais: A dataset for recognizing maritime imagery in the visible and infrared spectrums[END_REF] provides visible and infrared maritime images for ship classification [START_REF] Zhang | Classification of marine vessels with multi-feature structure fusion[END_REF] [START_REF]Convolutional neural network based on extreme learning machine for maritime ships recognition in infrared images[END_REF]. It includes more than 1,000 paired RGB and infrared images among six ship categories (merchant, sailing, passenger, medium, tug, and small). The largescale MARitime VEsseLs (MARVEL) dataset [START_REF] Marvel | A Large-Scale Image Dataset for Maritime Vessels[END_REF] has been introduced for classification of maritime vessels [START_REF] Leclerc | Ship classification using deep learning techniques for maritime target tracking[END_REF]. Although the dataset includes 2M images, the provided ground truth (GT) annotations contain the url to download image and the class label without bounding boxes. Also, the available scripts to retrieve the images from ShipsSpotting website [START_REF]shipspotting website[END_REF] is no longer functioning. Both VAIS and MARVEL datasets are not applicable for deep learning detection.

Singapore Maritime Dataset (SMD) has been published in [START_REF] Prasad | Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey[END_REF]. It compromises 81 videos, which are recorded using cameras either placed on-shore or fixed on-board of a moving vessel during daytime and nighttime. Also, the dataset includes thermal videos captured using a camera equipped by Near-IR bandpass filter. SMD annotations include 10 classes of maritime objects (ferry, buoy, vessel/ship, speed boat, boat, kayak, sail boat, swimming person, flying bird/plane, and Other). The GT labels for every frame of every video is provided comprising bounding-boxes and object classes for the corresponding bounding-box. This dataset has been evaluated in many research works such as [START_REF] Prasad | Object detection in a maritime environment: Performance evaluation of background subtraction methods[END_REF] and [START_REF] Moosbauer | A benchmark for deep learning based object detection in maritime environments[END_REF]. Recently, an improvement of SMD called SMD-Plus is described in [START_REF] Kim | Object detection and classification based on YOLO-V5 with improved maritime dataset[END_REF].

SeaShips [START_REF] Shao | Seaships: A large-scale precisely annotated dataset for ship detection[END_REF] is another published dataset for detection. It consists of 31,455 images including 40,077 annotations of six categories of marine ships (ore ships, bulk carriers, general cargo ships, container ships, fishing ships and passenger ships). The images are extracted from 10,080 video segments recorded by surveillance cameras placed in the coastline. Bovcon et al. have introduced the MODS dataset in [START_REF] Bovcon | Mods-a USV-oriented object detection and obstacle segmentation benchmark[END_REF] for unmanned surface vehicles (USV) obstacle detection. This dataset merges MODD1 [START_REF] Kristan | Fast image-based obstacle detection from unmanned surface vehicles[END_REF], MODD2 [START_REF] Peng | Development of the USV 'jinghai-i' and sea trials in the southern yellow sea[END_REF] and SMD [START_REF] Prasad | Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey[END_REF] datasets. Overall, the MODS dataset contains 24,090 images and 145,334 annotated objects. MaSTr1325 dataset [START_REF] Bovcon | The MaSTr1325 dataset for training deep USV obstacle detection models[END_REF] is published as a marine semantic segmentation training dataset for the application of obstacle detection in small-sized coastal USVs. MODS and MaSTr1325 datasets are used in the context of obstacle detection; thus, the objects are categorized into two classes only: small obstacle and large obstacle.

In [START_REF] Zheng | Mcships: A large-scale ship dataset for detection and fine-grained categorization in the wild[END_REF], Mcships dataset is introduced for ship detection and fine-grained categorization. The dataset includes 14,709 annotated images compromising 26,529 instances of ships which are categorized into 6 classes of warships and 7 classes of civilian ships. ABOships [START_REF] Iancu | ABOships-an inshore and offshore maritime vessel detection dataset with precise annotations[END_REF] is a recent publish dataset which consists of 9880 on-shore and off-shore images of maritime objects. Images in this dataset are categorized into boat, cargoship, cruiseship, ferry, militaryship, miscboat, miscellaneous, motorboat, passengership, sailboat, seamark.

III. TRAINING AND EVALUATION A. Collected Dataset

A diverse dataset of marine objects is created. Initially, we make use of the published datasets such as SMD [START_REF] Prasad | Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey[END_REF], SeaShips [START_REF] Shao | Seaships: A large-scale precisely annotated dataset for ship detection[END_REF] and MODD [START_REF] Kristan | Fast image-based obstacle detection from unmanned surface vehicles[END_REF] datasets. These datasets include images with high level of similarity as the images are extracted from recorded video sequences. This may impose over-fitting while training the neural network. Therefore, the datasets are cleaned by removing images that are too similar and bad images. Also, the annotations are checked and adjusted by fixing the imprecise bounding boxes and wrong labels. The merging of these datasets In addition to the images in the published datasets, we add new images of available classes and create new classes as well. The added images have different backgrounds, size, weather condition, light luminosity and show object from different points of view. The added images are taken from AIS providers 1 . To annotate new images, semi-automatic annotation is used. First, a neural network is trained using the images of public datasets. The trained network is then used to locate automatically the bounding boxes of marine objects in the new images.

During this step, images which are too similar in the same class are removed. Similar classes are merged to ovoid confusion while detection. This process is done attentively. For each iteration, a neural network is trained on the collected images considering all classes. Validation is done using images not seen by the trained model. A confusion matrix that represents the predictions in terms of a ground truth is created. Accordingly, classes with high confusion are merged.

As a result, our dataset includes 27,781 images compromising 35,140 annotations of 41 classes. Fig. 1 demonstrates the distribution of annotations per class of our dataset. The resulting dataset is the hugest available dataset for marine object detection in terms of number of classes and number of annotations. Note that the resulting dataset will be released to research community under request to facilitate progress in detection of marine objects using deep learning methods.

The dataset point of view is challenging due to two main reasons. First, in general, ships are very similar in shape (low interclass variation). Second, due to viewpoint, weather condition and illumination variations, but also due to scale changes, occlusion, cluttered background and so on, ships within the same class may be significantly different (large intra-class variation) [START_REF] Zheng | Mcships: A large-scale ship dataset for detection and fine-grained categorization in the wild[END_REF].

B. Target Models

In this work, YOLOv4 and YOLOv4 Tiny models are targeted. Table I presents the specifications of the targeted models. The depth of the layers at the input of YOLO layers in the networks are adjusted to fit with the number of classes. To meet with the supported architectures on Kria KV260 Vision AI kit, the activation function of YOLOv4 is modified from MISH to LeakyRelu. Also, the maxpool sizes of the spatial pyramid pooling (SPP) are changed to 3 × 3, 5 × 5 and 7 × 7 as the Deep Learning Processor Unit (DPU) has a maximum kernel size of 8 × 8.

C. Training

The training processes are conducted using Darknet framework [START_REF] Redmon | Darknet: Open source neural networks in C[END_REF] on Nvidia Quadro RTX 3090. Transfer learning is adopted by initiating the training with the weight values of models

D. Evaluation of trained models

The trained models are validated while training using the validation dataset. The mean average precision (mAP) is computed for each 4 epochs based on the AP50 metric defined in the MS COCO competition. Fig. 2 presents the training and validation performances. The blue curve in the figure corresponds to the training loss; whereas, the red curve corresponds to the computed mAP values. Fig. 3 shows sample predictions using the trained model.

In addition, the models are evaluated using several popular metrics in the field of object detection. Table II presents the obtained evaluation results for all trained models.

IV. STRUCTURED PRUNING USING SPARSIFYING

Pruning of the trained model at the level of channels and layers results in high-speed inference along with high-precision detection. Before starting the pruning, insignificant channels are identified using training under channel-level sparsity-induced regularization [START_REF] Tian | Pruning-based YOLOv4 algorithm for underwater gabage detection[END_REF]. L1 regularization of the loss during training is adopted based on the work presented in [START_REF] Liu | Learning efficient convolutional networks through network slimming[END_REF]. The cost function is modified by adding a penalty term on the scaling factor (sparse weights). Also, a parameter λ is used to balance the normal training loss and the penalty term defined on the weights. Different values of λ are examined using the collected dataset to determine the best value. In this case λ = 0.0015 is chosen. To determine whether the sparseness is sufficient, we use the Guppy multiple moving averages (Gmma) weight distribution map of each batch normalization (BN) layer. During sparsity training, it can be noticed that Gmma weights tend to close to zero indicating more sparseness as shown in Fig. 4.

Accordingly, channel pruning is performed to eliminate the channels with little contribution. The input-output connections and the corresponding weights of these channels are deleted. In this step, channels with near-zero scaling factors are pruned using a global threshold across all layers. A specific percentile of all the scaling factor values is used to define this threshold. To attain the most relevant value of the global threshold, the strategy of large intervals is adopted followed by subdividing gradually the intervals to achieve the optimal pruning point. In this work, the optimal pruning point is reached for 78% pruning.

Layer pruning is then performed in order to address the cross layer connections (residual) in YOLOv4 network. These special types of connections link the output of one layer to the input of several subsequent layers. The previous CBL (Conv + Batch Normalizaton + Leaky-Relu) of each shortcut layer in the network is evaluated. Accordingly, the Gmma mean of each layer is saved and the layer with the smallest value is selected for pruning. Many experiments are performed in order to find the most adequate shows that the mAP drops by 19.3 points after sparsity training. However, this degradation, which is due to the modification of the loss function, is compensated later by the conducted finetuning on the pruned network. Also, the results illustrate that the channel pruning greatly reduces the number of model parameters (-97, 06%) and the FLOPS (-88.87%) that involve a speed-up effect on embedded devices without suffering from accuracy loss.

Fine-tuning of the YOLOv4 network recovers the accuracy loss due to sparsity training. The obtained results illustrate that the fine-tuned network achieves a mAP of 77% while preserving a significant decrease in the required memory (-97, 08% reduction of parameters) and computations (-88, 88% reduction of FLOPS). Fig. 5 shows the obtained performance metrics while fine-tuning the compact model.

V. DEPLOYING TRAINED MODELS ON EDGE DEVICES

Deep learning-based object detection technology in embedded systems needs to be optimized for low latency and high ac- curacy detection rates and low power consumption. In general the deployment flow compromises two stages. In the first stage, the weights and/or activations are quantized to the desired bitwidth and representation (FP16, INT8). This is done using a heuristic method that benefits from selected pool of images from the training dataset which is so called the golden reference pool.

In the second stage, the quantized model is compiled to generate the instruction sequence. In this stage, the model is optimized according to the targeted device. Three different GPU-based, ASIC-based and FPGA platforms are considered in this study. Their hardware specifications are given in Table . IV, which presents the technical specifications of the embedded edge devices targeted in this work.

In the following subsections, we present the deployment of the trained YOLOv4 models on targeted embedded edge devices: (1) Nvidia Jetson Xavier AGX, (2) Xilinx-AMD Kria VISION AI KV260 and (3) Intel Movidius Myriad X Vision Processing Unit (VPU) integrated in OAK-D camera kit.

A. Deployment targeting Nvidia Jetson Xavier AGX

The trained models are deployed with TensorRT in order to achieve lower latency and higher throughput inference while running on Nvidia platforms. TensorRT is a software develeopment kit (SDK) provided by Nvidia for high performance deep learning inference. It is compatible with most deep-learning frameworks and is used to achieve high performance and platform portability. It compromises an inference optimizer that implements several techniques such as kernal fusion, precision calibration, kernel auto-tuning, dynamic tensor memory and multi-stream execution to optimize the inference of the trained model. Since Darknet framework is not supported by TensorRT, the target models are first converted using Open Neural Network Exchange (ONNX) and then to TensorRT engine with FP32 representation. Next, the target models are quantized to FP16 and INT8 representations. Table . V provides the obtained results in terms of detection performance and inference speed. The table shows a comparison between the original trained models and the converted models using TensorRT when deployed on the Jetson Xavier AGX. The comparison shows that using TensorRT increases the inference rate with slight degradation of mAP for all tested networks. In addition, the quantization leads to better inference rate. For YOLOv4 Tiny network, the inference rate is increased by ×1.55 and ×1.67 times but at the cost of degradation in the detection performance by 2.2 and 2.4 points in mAP for FP16 and INT8 quantization respectively. For YOLOv4 model, the FP16 quantization leads to increase the FPS by ×2.93 and ×8.47 times with a slight degradation in the mAP of 2.9 and 4.1 points for 416 × 416 and 608 × 608 input image resolutions respectively. Compared to the results of FP16 quantization, the quantization process for INT8 representation leads to an increase in inference rate by +6 and +4 FPS but at high cost accuracy degradation of 32.4 and 18.5 point in mAP. It is noted that the quantization of FP16 leads to same mAP value of FP32 but with better inference speed. Note that the FPS are recorded for input HD video with resolution of 1920 × 1080.

B. Deployment targeting Xilinx-AMD Kria KV260 AI Vision Kit

Vitis AI open source tooset does not support Darknet framework. Hence, the trained models are converted to a frozen TensorFlow graphs. Note that MISH activation function is not supported by the DPU in Xilinx FPGA. So, only models with LeakyRelu activation functions are deployed on Kria KV260 AI VIsion Kit. Using VITIS AI toolset, the converted models can be quantized into INT8 representation and then compiled targeting DPUCZDX8G architecture. Indeed, FP16 representation is not supported for Kria KV260 Vision AI kit (Table I). Table . VI shows the obtained results in terms of detection performance and inference speed of the deployed models on Kria KV260 kit. The quantized YOLOv4 Tiny model can achieve the highest inference speed (50 FPS) with a 4.5 points degradation in mAP. The quantized YOLOv4 model achieves the best mAP value (74.9%) on Kria KV260 kit with an inference speed of 11 FPS. The pruned YOLOv4 network can achieve 50 FPS but with significant degradation in mAP (-23.9 points). As a result, YOLOv4 Tiny would be the most convenient choice to run on Kria KV260 kit

C. Deployment targeting Movidius Myriad X VPU

The YOLOv4 and YOLOv4 Tiny models are also deployed on Movidius Myriad X VPU, which is programmable with the Intel distribution of the OpenVINO [START_REF] Ionica | The movidius myriad architecture's potential for scientific computing[END_REF]. The trained models using Darknet framework are first converted to TensorFlow freezed graphs. The converted model is optimized and quantized to FP16 representation using OpenVINO. Indeed, the INT8 representation is not supported for Movidius Myriad X VPU (Table I). Table VII presents the obtained results in terms of detection performance and inference speed. The results show that the FP16 quantized model of YOLOv4 Tiny achieves an inference speed of 31 FPS (2 threads running on 6 SHAVE cores) with a negligible degradation of 0.01% in the mAP (75.41%) when compared to the unquantized converted network (75.42%). The quantized YOLOv4 model achieves a speed rate of 3 FPS (2 threads running on 6 SHAVE cores) when processing a video of resolution 416 × 416. The degradation of mAP for the quantized YOLOv4 model is 0.02%

D. Analysis

The Jetson Xavier AGX is the most powerful device among the three platforms in terms of detection performance. It achieves the highest inference speed while maintaining acceptable precision values. However, it requires more power budget. It achieves 3.23 FPS/Watt for YOLOv4 Tiny and 1.46 FPS/Watt for YOLOv4.

The KRIA KV260 AI VISION KIT power consumption is 4 times less than the GPU on Jetson Xavier AGX. However, the achieved inference speed is 1.6 times and 4 times less than that achieved using the GPU for YOLOv4 Tiny and YOLOv4 respectively. It is necessary to realize a graph pruning to achieve 15 FPS with only one DPU B4096@300Mhz. While considering performance per watt criterion only, the KRIA KV260 kit outperforms the other targeted devices when running YOLOv4. It achieves 1.875 FPS/Watt.

The MOVIDIUS MYRIAD X VPU suffers from a lack of computing power, yet it is enough to infer a Yolov4 Tiny at 31 FPS with a power consumption less than 5 Watts with OAK-D camera. The comparison in terms of performance per Watt shows that for YOLOv4 Tiny, the VPU outperforms the other two devices as it achieves 7.75 FPS/Watt.

VI. CONCLUSION

This paper tackles the topic of marine object detection using deep learning techniques on embedded edge devices. A novel dataset with the widest number of classes and annotations is presented. Several YOLO models with different structure specifications are trained and evaluated considering different parameters such as image resolution and data augmentation. The deployment of the trained models on recent edge devices is considered. Several optimization techniques are applied to enhance the inference speed while maintaining high detection performance. The impact of these techniques in terms of FPS and precision is analyzed while targeting Nvidia Jetson Xavier AGX, AMD-Xilinx Kria KV260 Vision AI Kit, and Movidius Myriad X VPU. The obtained results show promising results. For example, 90 FPS inference speed is achieved on Jetson Xavier AGX with limited degradation of 2.4% in mAP.

Fig.

  Fig. Distribution of images per class

Fig. 2 .

 2 Fig. 2. Sample training and validation performances number of Resunits to be removed. According to the evaluation of resultant accuracy based on the metrics of precision, recall, mAP and F1-score of all experiments the best choice is to cut 20 Resunits which imposes the removing of 48 layers in total. At last, fine tuning is then performed to assist the pruned model to restore accuracy. The pruned compact model is trained again for 300 epochs.

Fig. 3 .

 3 Fig. 3. Sample predictions using the trained model

Fig. 4 .

 4 Fig. 4. Gmma weight distribution map of the BN layer

Fig. 5 .

 5 Fig. 5. Obtained performance metrics while fine-tuning the compact model

1

  MarineTraffic: https://www.marinetraffic.com/ previously trained on popular large-scale COCO dataset. Only the weights of the detector layers are cleared. This act preserves the generalization of the feature extraction layers in the output model. During training the number of batches is set to 32. The model is trained for 75 epochs. The training rate is adjusted to 0.001. A scaling factor of 0.1 is applied at iterations 65600 and 73800. The input images are down sampled into resolutions of 416 × 416 or 608 × 608. Several Data augmentation (DA) modes are applied during the training process such as mosaic, cutmix, rotation and changing exposure and saturation.

Table .

 . III illustrates the obtained results after each pruning step of YOLOv4 network with leakyRelu activation function. The table

TABLE V OBTAINED

 V RESULTS ON JETSON XAVIER AGX

	Network		YOLOv4 Tiny					YOLOv4
	Activation Function		LeakyRelu					MISH
	Input Resolution		416×416			416×416			608×608
	Model	original	FP32	FP16	INT8	original	FP32	FP16	INT8	original	FP32	FP16	INT8
	mAP VOC12	0.783	0.761	0,761	0,759	0,825	0.796	0,796	0,472	0,841	0.800	0.800	0,615
	FPS (HD video of resolution 1920×1080)	58	82	90	97	15	19	44	50	9	9	26	30

TABLE VI OBTAINED

 VI RESULTS ON XILINX-AMD KRIA KV260 AI VISION KIT

	Network	YOLOv4 Tiny		YOLOv4		Pruned YOLOv4
	Input Resolution		416×416			416×416			416 × 416
	Model	original	FP32	INT8	original	FP32	INT8	original	FP32	INT8
	mAP VOC12	0.783	0.744	0.738	0.829	0.796	0.749	0.77	0.634	0.531
	FPS HD video of resolution 960×540	-	-	60	-	-	11	-	-	50
	FPS (HD video of resolution 1920×1080)	-	-	15	-	-	11	-	-	15

  as it achieves the highest inference speed and highest mAP value. The FPS values for FP32 representation are not listed in Table VI as the DPU in Kria KV260 kit can run only values in INT8 representation. Note that the FPS values in the table corresponds to processing HD video frames using 4 threads.

			TABLE VII
	OBTAINED RESULTS ON MOVIDIUS MYRIAD X VPU
	Network	YOLOv4 Tiny		YOLOv4
	Input Resolution		416 × 416			416 × 416
	Activation Function		LeakyRelu			MISH
	Model	original	FP32	FP16	original	FP32	FP16
	mAP VOC12	0.783	0.7542	0.7541	0.825	0.7856	0.7854
	FPS (video of resolution 416 × 416)	-	-	31	-	-	3