V Djoufack

B Fontaine †

N Martiny

M Tsalefac

Climatic and demographic determinants of vegetation cover in northern Cameroon

The objective of the study was to evaluate the spatio-temporal impacts of seasonal rainfall and urban population growth on the variations in normalized difference vegetation index (NDVI) in north Cameroon, which includes climates from south to north, the Sudanese and Sahelian climates. To this end, 48 points of measured rainfall were interpolated based on the kriging method at a spatial resolution of 8 km in accordance with the NOAA-AVHRR NDVI data set. Relationships between rainfall and NDVI, on the one hand, and urban population growth and NDVI, on the other, were analysed considering the 79 administrative units (AUs) in Cameroon. Seasonal (rainy season) variations of the vegetation cover were stud-ied for the period 1987-2002 using the NDVI product at 8 km (NOAA-AVHRR) and 1 km (SPOT-VEGETATION) of spatial resolution. This article emphasizes the importance of the urban signal for the NDVI studies at finer scales, specifically in tropical areas.

Introduction

Vegetation indices are currently used to follow trends of vegetation cover at various temporal scales [START_REF] Pouchin | l'observation de la végétation de l'Afrique de l'Ouest par télédétection spatiale: l'apport de l'indice de végétation normalisé[END_REF][START_REF] Bigot | Contribution of HRV-SPOT data for the study of phenological changes in the watershed of Ouémé (Benin)[END_REF][START_REF] Hermann | Recent trends in vegetation dynamics in the African Sahel and their relationship to climate[END_REF][START_REF] Martiny | Interannual persistence effects in vegetation dynamics of semi-arid Africa[END_REF], Tucker et al. 2005), as well as to estimate biomass yields (Lambin et al. 2003) or crop yields [START_REF] Batholomé | Monitoring of rainfed cereals in Sahelian countries by remote sensing[END_REF][START_REF] Hochheim | Spring wheat yield estimation for western Canada using NOAA NDVI Data[END_REF][START_REF] Odekunle | Application of GIS to assess rainfall variability impacts on crop yield in Guinean savanna part of Nigeria[END_REF]. Relationships between vegetation indices such as the normalized difference vegetation index (NDVI) and climate parameters have shown that rainfall variability explains an important part of seasonal and intra-seasonal variations of vegetation activities in the Sudanese and Sahelian regions of Africa (Eklundh and Olsson 2003[START_REF] Hermann | Recent trends in vegetation dynamics in the African Sahel and their relationship to climate[END_REF][START_REF] Martiny | Compared regimes of NDVI and rainfall in semi-arid regions of Africa[END_REF][START_REF] Camberlin | Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa[END_REF]). However, changes in vegetation cover have also been linked to human activities, driven by socio-economic, cultural and demographic factors that also deserve to be explored. These anthropologic factors remain difficult to measure as they are much diversified and often connected to parameters that are not easy to quantify.

Northern Cameroon (or north Cameroon) is a particularly interesting region as it presents a juxtaposition of land-cover density and population density that are *Corresponding author. Email: vi3433dj@u-bourgogne.fr among the highest in the country as well as farming systems that are among the most intensive. The region is prone to climatic crises (intra-seasonal droughts, false starts and shortening of rainy seasons, etc.), which have direct consequences on the natural and cultivated vegetation cover. The leading climatic factor for vegetationcover variability in the study area is rainfall. The leading anthropologic factors for vegetation-cover variability are primarily similar to those in many countries in sub-Saharan Africa: wood cutting (for heating and for sale), farming and agriculture, all directly related to demography [START_REF] Dongmo | Human impact on the degradation of vegetation in north Cameroon[END_REF]. Therefore, to face up the unceasing growth of population at the rate of about 2.8% per year (Central Office of Census and Demographic Studies of Cameroon (BUCREP 2010)), cultivators need to increase the area under cultivation.

Since the 1950s, agricultural clearings have significantly increased [START_REF] Donfack | Fallow vegetation in northern Cameroon: typology, diversity, dynamism, production[END_REF][START_REF] Dongmo | Human impact on the degradation of vegetation in north Cameroon[END_REF][START_REF] Yengué | les mécanismes du déboisement et du reboisement dans le Nord du Cameroun[END_REF] and, because of the economic crisis, since the end of the 1980s there has been a strong rural exodus. However, unemployment and low wages has led the majority of citizens to invest in agriculture, especially on the outskirts of the cities. In addition, urbanization is the most striking demographic fact revealed by the final results of the last population census of Cameroon. Thus, the practice of agriculture is no longer exclusive to the population living in rural areas: floodplains such as the Diamare and Benoue are most coveted because of their greater fertility (Karal), especially for dry season crops, e.g. mouskouwari [START_REF] Mathieu | Le sorgho repiqué au Nord-Cameroun:de l'analyse des pratiques culturales à l'accompagnement technique des producteurs[END_REF]. In short, the vegetation cover (natural or cultivated) is considered a resource as it augurs good harvests and better pastures. So, it becomes necessary to identify the changes that affect vegetation in order to detect the most affected areas and to evaluate the sensitivity of vegetation cover to both climatic and demographic variations: this was the objective of this study conducted in northern Cameroon.

For this purpose, the NDVI [START_REF] Tucker | Red and Infrared linear combination for monitoring vegetation[END_REF]) was used as an indicator of the vegetation cover, and in situ rainfall measurements were used as the climatic parameter. The demographic parameter was the urban population, defined as people living in areas of at least 5000 inhabitants (WALTPS, ONU), because the urbanization phenomenon involves the implementation of economic infrastructure (roads, industries, housing) and substantial migration (of jobs seekers) that are directly or indirectly connected with vegetation changes inside and around urban centres [START_REF] Antoine | Urbanization in Africa and its perspectives[END_REF]. Given the successive droughts that occurred at the beginning of the 1980s, the study focused on the period after 1987 in order to remove the inter-annual memory effects of vegetation. The originality of this study lies in the fact that it has been carried out on a scale that allows us to detect changes on a restricted space (pixel) and to evaluate interactions between rainfall, NDVI and urban population growth in each administrative unit (AU) in order to derive elements that could be used in decision-making in terms of climatic and anthropogenic impacts on changes in land cover. After presenting the key elements of the geographical context ( §2), the data sets (NDVI, rainfall, urban population) and the main methodological approaches are presented ( §3). The results arediscussedin §4.

Geographical context

North Cameroon comprises a diversity of physical and natural conditions. Located between 6 • Na n d1 3 • N, and 11 • Ea n d1 6 • E, it includes, from south to north, the Adamaoua, north and extreme north regions, spanning a total area of about 164 000 km 2 (figure 1). [START_REF] Letouzey | Phytogeographical study of Cameroon[END_REF] and [START_REF] White | The Vegetation of Africa[END_REF]).

Vegetation

The vegetation cover (figure 1(a)), consisting of the Sudano-Guinean savannas and deciduous woodland south of 9 • N, becomes irregular and discontinuous northwards; the central part of the extreme north region is occupied by sparse grasses and thorny Sahelian steppes, whereas 'Sudanese savannas of Mountains' [START_REF] Letouzey | Phytogeographical study of Cameroon[END_REF][START_REF] White | The Vegetation of Africa[END_REF] were found in the region of the Mandara Mountains. The eastern part of the far-north region is covered by floodable grassland.

Topography

Three main units can be distinguished (figure 2(b)): the Mandara and Alantika Mountains, from 900 to 1885 m, in the western part of the northern zone, between Cameroon and Nigeria; the Adamaoua plateaux (∼1000 m) in the southern part of the region, which shelter mountains reaching up to 2500 m (figure 1(b)); and the plains (altitudes below 300 m), drained by the permanent rivers of Benoue and Logone and practically flooded from July to November.

Climate

North Cameroon is under the influence of the African monsoon and is dominated by the seasonal translation of the ITCZ (inter-tropical convergence zone) and by the Harmattan winds coming from the Sahara. It can be divided into two sub-climatic areas: the Sahelian zone stretching over the 8th parallel and the Sudanese (or Sudano-Sahelian) zone located southwards. The mean annual temperatures vary from 28 • Cin the Sahelian region to 24 • C in Adamaoua, whereas the monthly maximums are 34 • C and 28 • C, respectively. The mean amount of annual rainfall ranges between 500 and 1500 mm, and shows a strong inter-annual variability (figure 6).

Population

According to the results of the last population census of Cameroon (2010), the size of the urban population had been increasing by 3.9% between 1976 and 2005, highlighting a particularly strong urban dynamism. In addition, the WALTPS (West African Long Term Perspective Study) showed that about 50% of the population of Cameroon lived in urban areas in 1995 and that this urban population was increasing by about 4.9% per year [START_REF] Antoine | Urbanization in Africa and its perspectives[END_REF]. In 2007, the north Cameroon population was estimated at nearly 5 700 000 (BUCREP 2008), i.e. an increase of more than 3 million since the population census of 1976. However, the spatial distribution of the population in that region is striking: the most populated regions are found in the capital town of each sub-region unit, specifically in Maroua, Garoua, Ngaoundere, Kousseri and Mora (refer to figure 2(c) to locate these places), whereas the countryside is often depopulated [START_REF] Marguerat | The urban organisation in sub-saharan Africa: from the pyramid to macrocephaly[END_REF]. The populations of the extreme north, north and Adamaoua regions represent 18%, 10% and 5%, respectively, of the total population of the country.

Data and methods

Normalized difference vegetation index

Vegetation indices obtained by remote-sensing methods are widely used today as indicators of vegetation photosynthetic activity, which can be representative of the vegetation cover of a region when the indices are time-integrated. These indices are computed based on the combination of spectral bands shown to be related to green biomass [START_REF] Tucker | Red and Infrared linear combination for monitoring vegetation[END_REF]). The one used in this study is the NDVI, which is based on the ratio between the difference and the sum of the top-of-atmosphere reflectance in the 'near-infrared' (NIR) and the 'visible' (RED) spectral bands: NDVI = ( N I R-R E D ) /(NIR + RED). These reflectances are those of the AVHRR sensor (Advanced Very High Resolution Radiometer) of the NOAA (National Oceanic and Atmospheric Administration) satellites, compiled during various missions: NOAA-7 (1981NOAA-7 ( -1985)); NOAA-9 (1985-1988and August 1994-January 1995); NOAA-11 (1989NOAA-11 ( -1994)); NOAA-14 (1995NOAA-14 ( -2000)); and NOAA-16 (since 2000). The data set is broadcast by Global Inventory Monitoring and Modelling Systems (GIMMS) and includes 15-day synthesis maps of NDVI at 8 km spatial resolution. Those bimonthly synthesis maps include corrections for calibration bias, orbital drifts and geometrical viewing/illumination conditions and stratospheric aerosol. The bimonthly integration of the NDVI enables us to remove the effects of clouds [START_REF] Tucker | An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data[END_REF]. NDVI data were extracted on the 'window' of north Cameroon for a total of 2497 points (97 • latitude and 53 • longitude for each time period) from 1987 to 2002.

For each of the 16 years, the mean NDVI of the rainy seasons was computed, i.e. those where the average cumulative rainfall exceeds 50 mm [START_REF] Suchel | les climats du Cameroun[END_REF]: in the Sahelian region, the rainy season being defined in May-September and April-October for the region southwards to 9 • N. The interest in working on seasonal average charts of NDVI during the subsequent rainy seasons was justified by the fact that it was the most active period for vegetation [START_REF] Martiny | Compared regimes of NDVI and rainfall in semi-arid regions of Africa[END_REF]. It was thus easier to separate various classes (or clusters) of vegetation at this period, although the distinction between woody savannas and crops remain fuzzy in the Sudanese areas (Achard and Blasco 1990). After having constituted seasonal NDVI meta-files of the 16 years, the next concern was to evaluate for each pixel the changes that occurred during the period 1987-2002.

The second NDVI data set we used comes from the VEGETATION (VGT) instrument of the Système Probatoire d'Observation de la Terre (SPOT) satellite, which ensures daily global coverage of the Earth's surface with a 1 km spatial resolution. Data sets are broadcast by the Vlaamse Instelling voor Technologisch Onderzoek (VITO), which routinely carries out angular and atmospheric corrections on data that were available in 10-day composite NDVI and from 1 April 1998 to 31 October 2009. Values are encoded and the real NDVI values are derived using the following formula:

NDVI =-0.1 + 0.004X (X represents encoded value) .( 1 )
Further information and VITO's data are available at www.vgt.vito.be. This database was used to study efficiently the land-cover changes into urban areas, given their high spatial resolution. To do this, only years with complete data were taken into account, i.e. from 1999 to 2008.

NDVI classifications.

The various methods of regionalization that use NDVI are based on the fact that each type of vegetation has its own spectral signature and may have a distinct photosynthetic activity with respect to rainfall. Thus, the regionalization methods allow us to discriminate between the main vegetation types. The isodata method was used in this study, and is a non-supervised approach: the isodata algorithm compares radiometric values of each pixel based on initial cluster vectors, primarily assigned (maximum and minimum threshold), as well as the minimum aggregates by cluster and the minimal distance between classes [START_REF] Tou | Pattern Recognition Principes[END_REF]Gonzalez 1974, Lacombe 2007). At each iteration, averages are re-computed and pixels are re-classified according to new averages. The operation ends either when the preset number of iterations is reached or when the rate of changes in each cluster becomes lower than the fixed threshold. Post-classification operations enable us either to compact classes by eliminating isolated pixels or by merging classes presenting similar characteristics or remove those presenting non-realistic configurations. Note that the numbers of cluster vectors were assigned based on the main vegetation types of north Cameroon [START_REF] Letouzey | Phytogeographical study of Cameroon[END_REF][START_REF] White | The Vegetation of Africa[END_REF].

Extraction of the NDVI in urban regions and out of urban areas.

Because our objective was to focus on small scales, a spatial resolution of 8 km seemed inaccurate to evaluate the impact of urbanization on vegetation. For that reason, the 1 km SPOT-VGT NDVI was used. Because the exact sizes of all of the cities were not available, the regions of interest (ROIs) over urban areas were selected from geographic information system (GIS) software, based on the population size of each AU (table 1).

Each ROI was constituted by the pixel containing the city itself, as well as the adjacent pixels that can be considered suburbs. In order to take into account the nonurban areas (rural), a mask was applied on urban areas previously located (cities and suburbs). Then, a radius of 4 km (equivalent to 4 pixels) was considered around those urban areas. The averages of those pixels were considered the NDVI of rural areas.

Rainfall data and spatial interpolation at an 8 km spatial resolution

Seasonal rainfall data of 48 stations (31 located in north Cameroon and 17 others in Chad, Nigeria and the Central African Republic) were selected to generate regular grids of rainfall at an 8 km spatial resolution in accordance with the NOAA-AVHRR NDVI grid. Most of the data of north Cameroon belong to the SODECOTON network [START_REF]Multi-scales analysis of rainfall variability in Cameroon, and its impacts on cotton yields[END_REF], whereas other data were given by the direction of the national meteorology as well as by the departmental offices of meteorology of countries mentioned above. Several spatial interpolation methods were tested with the aim to find the one that will be closer to reality. The kriging method was finally selected. A kriging algorithm analyses statistical variations of values over different distances (closer points are weighted more heavily than distant points) and different directions (anisotropy) with the intention to determine those that will produce the minimum error in the rainfall estimate [START_REF] Cressie | Statistics for Spatial Data[END_REF]. It was initially checked (i) whether the average and variance of points were stationary, i.e. they only depend on the distance between points and (ii) whether there was a spatial coherence in rainfall distribution over the region. So, the spatial continuity of data was computed on points for which values were known and the degree of reliability of results can be expressed as an experimental variogram (or semi-variogram). Values of the variogram were computed by averaging the squared differences of all pairs of observations with the specified distance and direction (for further details, see [START_REF] Oliver | Kriging: a method of interpolation for geographical information system[END_REF], [START_REF] Gratton | The kriging: an optimal method of spatial interpolation[END_REF], [START_REF] Baillargeon | The kriging: review of theories and applications for spatial interpolation of rainfall data[END_REF], [START_REF] Joly | Interpolation based on local information[END_REF]). To obtain the results, the experimental variogram plot needs to be adjusted because its shape serves only as a basis for determining the appropriate model. Cross-validation is an approach to estimate how well the model chosen is going to perform for the yet unseen data. In this study, the linear model was the one that adjusted our variogram the best, i.e. where the error between the estimated and observed rainfall was the weakest. Then, using GIS, the data were re-adjusted on the north Cameroonian 'window' and gridded at an 8 km spatial resolution.

Standardized indices of each parameter (NDVI or rainfall) were computed using the formula below, where ny is the number of stations of year y, p y s is the corresponding parameter for year y and station s; p s is the seasonal mean of the considered parameter for station s;andσ s is the seasonal standard deviation of the considered parameter for station s. Results were expressed in standardized anomalies, computed as follows: (3)

Results and analysis

Spatial patterns of NDVI in northern Cameroon (1987-2002)

Figure 2(a) displays the results of the classification (isodata) based on the mean NDVI annual regimes in northern Cameroon over the period 1987-2002. Referring to the prevailing land-use/land-cover type (GLC2000), as well as the cartography of the main types of 'natural' vegetations (figure 1(a)), the five NDVI clusters that result from the classification can somehow be matched with the main vegetation types of this region (figure 2(b)). Indeed, there was a gradation of vegetation from north to south: from 0.4 in the steppes (classes 1 and 2) to up to 0.7 in shrub lands and woodlands of the southern region (classes 3-5). Although clusters can be constituted by different vegetation species (e.g. mosaics), the signal displayed refers to the dominant species. So, the averages of NDVI are a smoothing (often non-realistic) of data, which consist of reducing the variance. That is why it was interesting to consider other parameters such as maxima, minima or standard deviations that indicate the dispersion of NDVI in the different classes (figure 2(b)). All of these regions are over-exploited for agriculture and cattle livestock, with more damaging environmental consequences in the northern part. At the inter-annual time scale (figure 3(a)), all classes emphasized an increase in NDVI at the end of the period 1987-2002. In early periods, classes 1 and 2 showed negative anomalies, whereas classes 3-5 were in surplus over the first four years of the period (>1) and in deficit in the 1990s. The NDVI mean annual regimes presented an unimodal profile (figure 3(b)), with a flattened shape for classes 3-5. However, the duration and amplitude of the rainy season have changed considerably from north to south: maxima were observed in August with an increase in NDVI at the peak date, from 0.5 (north) to 0.7 (south); an early onset and an early shrinkage of vegetation were observed in northern classes (1 and 2), whereas there was an opposite situation in southern classes (3-5).

In summary, the vegetation of northern Cameroon is very diverse in terms of typology, annual cycle and inter-annual variations. Significant variations are also observed between urban areas. The average NDVI (1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002) in AU capitals show large variations from one city to another, even in those belonging to the same class of NDVI (figure 4), but a mean difference by 8 • N(∼0.2) clearly appears.

What are the parameters underlying those spatio-temporal changes in NDVI during the period 1987-2002? To answer this question, the spatial evolution of rainfall was initially explored, and the sensitivity of vegetation to rainfall variability was examined using the rainfall/NDVI correlations. Then, the anthropogenic impacts were studied through the urban population growth. The analyses were done under two assumptions:

-positive NDVI anomalies induce an above-normal vegetation activity that can be associated either with an increase in rainfall or with the conversion of fallow land or wasteland (with lower reflectance) into farmland [START_REF] Beauvillain | Northern Cameroon: people and crisis[END_REF][START_REF] Gonné | Migrations and conséquences of the recent influx of farmers in the valley of Benue (northern Cameroon)[END_REF]); -conversely, negative anomalies imply strengthening of activities that promote vegetation degradation; this can be due to decline in rainfall or due to some processes associated with urbanization, inducing the cutting and uprooting of trees to build new urban infrastructure or new residences (Dongmo 1998, Ndame and[START_REF] Ndame | croissance urbaine, mutations agricoles et dépendance alimentaire dans le Nord-Cameroun[END_REF]. These results raise the question of a probable re-greening in the Sahel since the early 2000s [START_REF] Ozer | Discussion of analysis of sahelian annual rainfall index from 1896 to 2000: the drought continues, the sahelian drought may have ended during the 1990[END_REF][START_REF] Olson | Recent greening of the Sahel trend, patterns and potential causes[END_REF], Lebel and Ali 2009). Briefly, the rainy season extends from April to October in the Sudanese region and from May to September over the Sahelian area. These spatial particularities will be considered in the various analyses throughout the study. Does the localization of the maximum of rainfall in August as for the NDVI testify to the absence of lag between the rain and the NDVI? The answer to this question required a comparison of the two parameters provided that they are at the same spatial and temporal scale.

First explanatory factor: the rainfall

Spatio-temporal variations of rainfall (1987-2002).

Spatial interpolation of rainfall data at a spatial resolution of 8 km.

Figure 7 shows the scatter plots (44 points) observed versus estimated rainfall over the period 1987-2002 along with the regression line. It emerges that simple linear correlations were strong and significant as shown by the 'line of best fit' (r = 0.89) reflecting both the quality of the adjustment model in the semi-variogram and the greater consistency in the spatial distribution of rainfall over the period.

Composite analysis of rainfall and NDVI. (a)

In the whole region, figure 8 displays the mean rainfall/NDVI relationship considering 2497 pixels over the period 1987-2002. There is a gradual, simultaneous increase in both the parameters up to 1000 mm of rain while the NDVI is around 0.6. Above this threshold, the NDVI (between 0.6 and 0.7) remains constant independently of the increase in rainfall. This saturation phenomenon beyond 1100 mm is due to the physiological properties of plants that become non-responsive to rainfall variations when they have already reached a certain stage of phenological development. In this situation of hysteresis, rainfall is no longer a constraint for plant growth. The saturation threshold of NDVI varies according to bioclimatic characteristics as well as to the considered spatial scale. The threshold of saturation is often fixed at 1000 mm in east and west Africa [START_REF] Malo | A study of the rainfall and vegetation dynamics in the African Sahel using the Normalized Difference Vegetation Index[END_REF], Davenport and Nicholson 1993, Eklundh and Olsson 2003[START_REF] Hermann | Recent trends in vegetation dynamics in the African Sahel and their relationship to climate[END_REF][START_REF] Camberlin | Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa[END_REF][START_REF] Seghieri | Relationships between climate, soil moisture and phenology of the woody cover in two sites located along the West African latitudinal gradient[END_REF]. Consequently, synchronous NDVI/rainfall correlations were around 0.7 when computed over the whole region, but this value can be improved over the Sahelian belt if the rainfall leads the NDVI by approximately 1 month [START_REF] Shinoda | Seasonal phase lag between rainfall and vegetation activity in tropical Africa as revealed by NOAA satellite data[END_REF][START_REF] Djoufack | Seasonal NDVI in northern Cameroon and its relationship with rainfall (1982-2002[END_REF]. -in non-urban areas, the rainfall/NDVI relationship is quasi-natural and reflects the spatial distribution of the two parameters (figure 8); -in urban areas, the correlation seems to be sensitive to the importance of cities.

Two groups of points have been clearly distinguished: NDVI below 0.35 corresponds to the largest cities in terms of urban infrastructure. Beyond 0.35, the weight of urbanization is less prominent. This result suggests that local NDVI values are likely to be biased by the urban signal. To verify this hypothesis, urban population data were examined as an indirect factor in cities' typologies, while knowing that more (less) populated city areas are more (less) important in terms of urban infrastructure.

Second explanatory factor: the urban population growth

Spatial distribution and evolution of the urban population in north Cameroon.

Given that phenomena such as urbanization are better apprehended by using high spatial resolution products, the NDVI data (SPOT 1 km) were used to study the relationship between NDVI and urban population. Because of the low temporal depth of these data, the study was undertaken over a 10-year period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008).

As in most countries of sub-Saharan Africa, the main characteristic of the spatial distribution of urban population in north Cameroon is 'macrocephaly', i.e. the overwhelming preponderance of a single city over the rest of the urban system [START_REF] Marguerat | The urban organisation in sub-saharan Africa: from the pyramid to macrocephaly[END_REF].

Indeed, in 2005, nearly 40% of the urban population of northern Cameroon was concentrated in the cities of Maroua, Garoua and Ngaoundere (>590 000 inhabitants), which also represent the capital cities of the three regions (figure 10(a)). These cities were followed by Kousseri and Guider, in which the urban population in 2005 was estimated to be around 89 000 and 52 500, respectively. Indeed 47 cities have urban populations under 10 000. Also, compared to the two other regions, the far north shows the highest urban densities due to the small areas of the AUs: this is one of the reasons for the high land crises in that region.

Figure 10(b) shows the evolution of the urban population in AU capitals between 1999 and 2008. In 10 years, 19 AUs experienced a population increase from 40% to 50%, whereas 22 AUs registered a population increase between 10% and 20%. The decline in urban population relates to only six AUs. It should also be noticed that the highest growths do not concern the most populous cities such as Maroua, Garoua and Ngaoundere (30%, 17% and 20%, respectively), but cities such as Djohong, Madingring, Zina, Blangoua and Datcheka, which increased their population by more than 45% in 10 years. Besides, Guider and Mokolo, which represent the 6th and 10th most populous cities of northern Cameroon, had only growths of 7% and 12%, respectively. This rapid rise in the population of cities considered as rural areas some years ago can be explained by natural growth (through birth) and more importantly by the urban exodus due to high urban unemployment. Population of a few towns located north of the eighth parallel increased by more than 30% in 10 years. South of this latitude, urban population growth over 30% only took place in the cities of Djohong, Madingring Kontcha and Ngaoui. Elsewhere, urban growth did not exceed the threshold of 30%.

Composite analysis of NDVI/urban population growth relationships.

For each AU, the correlation coefficients between NDVI and urban population were calculated at the inter-annual time scale (79 AU × 10 years), for a set of 790 data for each parameter. Correlations were analysed and tested through Monte Carlo simulations at 95% confidence level. The results presented in figure 11 show that beyond the six cities experiencing population decrease, the urban demographic evolution in northern Cameroon is an exponential function. Based on this observation, it can be predicted that the relationship between NDVI and urban population growth will be the inverse, i.e. urban growth would imply a decrease in NDVI in urban areas. What about results?

The results have shown that, both parameters evolve in opposite ways, i.e. annual values of the NDVI decrease as the population increases. However, following the 10year period, significant correlations (R =-0.32 to -0.9) were observed for cities such as Ngaoundal, Kaele and Gobo, where the urban expansion was between 8000 and 30 000 inhabitants, and for those cities in which population increased by at least 30%, such as Mora, Ngaoui and Madingring. In both the cases, the vegetation cover decreased to the benefit of processes concomitant to population growth such as housing, political and economic infrastructure, etc. For cities such as Mbé, Taibong or Dembo, where the urban population remained low (<8000 inhabitants), and those where the population regressed (Zina, Doukoula, Porhi), the NDVI behaved the same way as it had in non-urban areas. In this case, NDVI variations seem to be more strongly related to rainfall, as shown in figure 9(a). Finally, the correlations between NDVI and urban population are weaker, i.e. NDVI values remain almost constant from year to year in more populated cities such as Maroua, Garoua, Ngaoundéré or Kousseri, where population has not increased significantly. Indeed, in those cities, the land-use process evolves in a gradual way compared to other regions where the rapid population growth entailed important changes in land use.

Significant correlations were found in both northern and southern cities, but the urban signal explains a larger part of vegetation-cover variations northwards to 9 • N, where 19 AUs (out of 46 AUs) show no significant NDVI/urban population correlation. To the south of this region, the urban signal is equally important because only five AUs have not shown significant correlation.

These results might reflect the fact that NDVI variations are less sensitive to the number of inhabitants than to the urban extension caused by population growth. This is the reason why NDVI studies at the local scale require some caution. What also emerges from this study is that the demography of urban areas was growing sharply, particularly in cities still considered rural a few years ago. The populations of most of the cities located northwards of 8 • N experienced a more than 30% increase between 1999 and 2008. In these localities, the NDVI-urban population enlargement relationship was significant because the increase is followed by a decrease in vegetation cover through the establishment or the expansion of urban infrastructures. Southwards to 8 • N, the correlations were less significant in some cities such as Meiganga, Mayo-Darle and Banyo. This means that, in those specific cities, the vegetation dynamics are related to factors other than rainfall and urban population growth, because the vegetation cover is less dependent on rainfall.

It is noteworthy that these correlations have been computed at the cities' spatial scale because urbanization is known as one of the poles of vegetation degradation and more specifically of deforestation [START_REF] Defries | Deforestation driven by urban population growth and agricultural trade in the twenty-first century[END_REF]. Indeed, the dense population of urban areas and agricultural trade are the main drivers of deforestation in tropical regions, whereas the population of rural areas is not correlated with deforestation, indicating the importance of urban-based and international demands for agricultural products as drivers of deforestation.

Conclusion

The objective of this study was to evaluate the factors in vegetation-cover variations through NDVI at 8 and 1 km resolutions in northern Cameroon. With this intention, two parameters were tested: precipitation, because of its well-known relationship with NDVI, and urban population growth, considered an indirect factor for vegetationcover decrease in urban areas and suburbs. Seasonal charts of NDVI at 8 km of spatial resolution were used as indicators of vegetation, whereas punctual rainfall data were interpolated, based on a kriging method at 8 km of spatial resolution according to the spatial resolution of NDVI data from 1987 to 2002. The study was focused on 79 AUs of northern Cameroon and dealt with the following three aspects.

1. Inventory of vegetation-cover changes. The main spatial patterns of NDVI as well as their annual and inter-annual evolution over the period 1987-2002 were described. The five spatial classes (cluster) of vegetation obtained from NDVI classification reproduced the vegetation type gradient from north to south. These clusters were different from each other by their inter-annual and annual characteristics: annual profile, start and duration of the greening season, interannual anomalies, etc. At the city scale, the NDVI also varies widely even in cities belonging to the same cluster. 2. NDVI/rainfall relationship. The changes in NDVI were explained using the spatio-temporal evolution of rainfall. The NDVI/rainfall relationship has shown better positive correlations northwards to the 1000 mm annual isohyets, confirming that rainfall variability clearly explains a smaller part of the vegetation-cover dynamics southwards to 8 • N, where the amount of rainfall is greater. 3. NDVI/urban population growth relationship. NDVI changes were studied in urban areas that have shown a significant increase in population over the period 1999-2008, in order to assess the impact of urbanization on vegetation.

The NDVI/ urban population relationship is based on 10-year (1999-2008) data, due to the shallow temporal SPOT-NDVI data at 1 km resolution. The results show good correlations for both moderately populated cities (8000-30 000 inhabitants) and those cities that have experienced a rapid increase in population (>30%). In contrast, for the most populated cities such as capitals of regions, the urban signal is strong and NDVI values, although weak, remained constant despite changes in population. In the sparsely populated towns (<8000), the urban signal is weaker, so vegetation evolution depends more on rainfall or land-use related factors south of the eighth latitude. Thus, NDVI studies at local scales are often biased by anthropogenic factors in and around urban areas and therefore such studies call for some caution. Our results showed that NDVI variations are less sensitive to the number of inhabitants than to the urban expansion caused by the population growth. These preliminary findings enable us to better understand the determinants of vegetation-cover change in northern Cameroon and, more generally, in the Sahelian and Guinean areas. Therefore, they suffer from methodological constraints because the NDVI spatial resolution of 8 or 1 km cannot detect changes below the size of that pixel. Consequently, the reflected signal is the proportion of photosynthetic radiation absorbed by the dominant land cover. A pixel is thus likely to gather several types of land use, which are often the results of complex anthropogenic or bioclimatic processes. This limit arouses one's interest to take into account products with a higher resolution, such as the Landsat data set at a 30 m spatial resolution available since 1972.

It would also be highly interesting to explore the impact of other demographic factors such as the rural population growth or migrations, particularly the seasonal movements of livestock (transhumance) that play a significant role in land use [START_REF] Boutrais | Upland livestock of Cameroon[END_REF]. Additionally, the organization of human activities in northern Cameroon is generally closely related to ethnic origins: Islamic people (Peulh, Arabs) are basically cattle raisers or nomadic pastoralists whereas the non-Muslim populations are essentially farmers. The association between agriculture and livestock is made for socio-economic, cultural or health reasons and is very often mired by numerous land conflicts (Blanc-Pammard and Cambrézy 1995). So, it would be interesting to include those parameters to understand the mechanisms that are associated with land use. Discussions are ongoing to include such hypotheses using higher resolution satellite products and possibly field surveys.

Figure 1 .

 1 Figure 1. Some physical characteristics of northern Cameroon: (a) distribution of vegetation cover; (b) spatial distribution of altitude and hydrography (Adapted from[START_REF] Letouzey | Phytogeographical study of Cameroon[END_REF] and[START_REF] White | The Vegetation of Africa[END_REF]).

  Demographic dataThe demographic data set consisted of the urban population of 79 AUs that represent regions, sub-regions and districts of northern Cameroon. These data come from the Population Census report of years 1987 and 2005, as well as demographic surveys of1998, 2002, 2007 and 2010, carried out by the National Institute of Statistics (NIS, for the year 2002), the National Office of Accounting and Statistics (DSCN, for the year 2002) and the BUCREP (for the years 1998, 2007, 2010). Demographic projections of intermediate years were computed based on the rate of growth of the urban population in each AU. The rate of population change was computed using the following formula: Population evolution = Population of year 2008 -Population of year 1999 Population of year 2008 ×100.

Figure 3 .

 3 Figure 3. Annual and inter-annual variations of NDVI in northern Cameroon (mean of the period 1987-2002): (a) standardized anomalies of NDVI of five different classes (refer to figure 2); black curves represent moving averages (over 2 years) and (b) annual cycle of NDVI in each class.

Figure 4 .

 4 Figure 4. Means of NDVI into AU capitals (mean of the period 1987-2002); the AU capitals are sorted from the south to the north.

Figure 5 .Figure 6 .Figure 7 .Figure 8 .

 5678 Figure 5. The main characteristics of rainfall in northern Cameroon. (a) Spatial distribution of rainfall (mean of period 1987-2002); (b) annual cycles

  (b) In urban and non-urban areas, figure 9 displays the rainfall/NDVI relationship in rural (figure 9(a)) and urban (figure 9(b)) areas. The urbanization explains the decrease in NDVI in urban areas and the presence of a large number of points around 0.25 despite increased rainfall. This requires two assumptions:

Figure 9 .

 9 Figure 9. The highlighting of the impact of urbanization in urban and suburb areas. NDVI/rainfall correlations (r)inrural(a) and urban (b)areas .

Figure 10 .Figure 11 .

 1011 Figure 10. The urbanization in northern Cameroon: (a) the urban population distribution in 2005; (b) evolution (%) of the urban population from 1999 to 2008; refer to figure 2 for names of cities.

Table 1 .

 1 Extraction of NDVI in urban areas, according to the size of the population.

	Population size (inhabitants)	Region of interest (ROI) area (km 2 )
	≤10 000	9
	[10 000-20 000]	16
	[20 000-30 000]	50
	>70 000	81

Acknowledgements

We are grateful to the Global Inventory Monitoring and Modelling Systems (GIMMS) group and the Vlaamse Instelling voor Technologisch Onderzoek (VITO), which provided NDVI data, and the National Meteorology offices of Chad, the Central African Republic, Nigeria and Cameroon for providing rainfall data. Special thanks also go to J. B. Djoumessi for his invaluable assistance in the acquisition and analysis of demographic data.

The study was supported by the Global Change and Ecosystems programme (EU Integrated project: African Monsoon Multidisciplinary Analysis (AMMA) and the French component of AMMA). Based on a French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from France, UK, USA and Africa. Detailed information on scientific coordination and funding is available on the AMMA International web site http:// www.amma-international.org.


Wided Hammedi
email: wided.hammedi@u-bourgogne.fr

Metzli Ramirez-Martinez
email: metzli.ramirez-martinez@u-bourgogne.fr

Sidi Philippe Brunet
email: philippe.brunet@u-bourgogne.fr

Mohammed Senouci
email: sidi-mohammed.senouci@u-bourgogne.fr

Mohamed Ayoub Messous
email: mohamed-ayoub.messous@u-bourgogne.fr

Deep Learning-Based Real-time Object Detection in Inland Navigation

Keywords: Real time, Object detection, Intelligent vehicles, Inland waterway vessels, Deep learning

Semi-autonomous and fully-autonomous systems must have knowledge about the objects in their environment to ensure a safe navigation. Modern approaches implement deep learning techniques to train a neural network for object detection. This project will study the effectiveness of using several promising algorithms such as Faster R-CNN, SSD, and different versions of YOLO, to detect, classify, and track objects in near real-time fluvial domain. Since no dataset is available for this purpose in literature, we first started by annotating a dataset of 2488 images with almost 35 400 annotations for training the convolutional neural network architectures. We made this data set openly accessible for the community working on this area. The other contribution of this research is the adaptation and the configuration of deep learning techniques used in other domains such as maritime and road domain to fluvial domain for autonomous vessels in which high accuracy and fast processing are vital. Experiments demonstrated that detecting objects in such environment is plausible in near real time with the selected algorithms.

I. INTRODUCTION

Inland water transport is a viable alternative to road and rail transport on European corridors. It is highly competitive with respect to other modes of transportation, environmentallyfriendly, reliable, safe and could provide lower transportation costs when moving large volumes of bulk cargo. However, the specificities of the waterways bring as many opportunities as new challenges for the automation of the transport vessels. Autonomous vehicles have made significant inroads into the area of transportation, but specifically in the terrestrial and marine environments. Thus, the idea in this work to study the performances of the methods used in these latters in inland environment in order to ensure a safe navigation of an autonomous vessels.

Specially, the object detection module of autonomous vessels plays an essential role in safe maritime navigation. The vessel needs to detect and avoid other nearby vessels and infrastructure. The current research trend is employing deep learning algorithms to learn useful features instead of handdesigning them. In fact, since the rise of Convolutional Neural Networks (CNNs) within the ImageNet [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF] challenge, they have gotten to be the foremost prevalent arrangement for com-mon question acknowledgment issues such as classification, localization, and location. They accomplished lower blunder rates than the past state-of-the-art results. For this reason, CNNs have been widely adopted in object recognition systems in almost all domains in [START_REF] Aiswarya | A convolutional neural network for visual object recognition in marine sector[END_REF], [START_REF] Tseng | Combination of Computer Vision Detection and Segmentation for Autonomous Driving[END_REF] and [START_REF] Fulton | Robotic detection of marine litter using deep visual detection models[END_REF]. The objective of the current work is to compare and identify the most appropriate algorithm for object detection in a fluvial environment in terms of accuracy, run-time and resources consumption. The novelties of this paper, compared to the state of art, consist of:

We produce a unique dataset for training deep visual detection models for object detection in inland environment, since there is no public dataset is available, We evaluate the accuracy and performance of six state-of-the-art object detection algorithms for the problem of object detection in inland environment. The rest of this paper is structured as follows. Section II gives an overview on the object detection problem and existing public datasets. In Section III, we detail the characteristics of our proposed dataset. Then, we describe the training process implementation in Section IV. Finally, discussion on results and performance evaluation are drawn in Section V and Section VI.

II. RELATED WORKS

In this section, object detection models, datasets and areas of application are reviewed. We mainly focus on the suitability of integrating the existing approaches to our problematic.

1) Object Detection: Computer vision has been especially a curiously intrigue field in later a long time since self-driving vehicles have taken centre stage. Generic object detection aims at locating and classifying existing objects in any one image and labeling them with rectangular bounding boxes to show the confidences of existence. Nowadays, the performance of object detection has been improved with the Deep CNNs methods which have recently come to dominate object recognition re-search due to their excellent performance on many challenging datasets harvested from the web, such as ImageNet [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF]. They achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is impressively superior than the past state-of-the-art [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF]. Among these methods, Faster R-CNN, SSD and YOLO show a better performance of detecting objects with different sizes in other application domains such as recognizing the types of marine vessels in sail [START_REF] Aiswarya | A convolutional neural network for visual object recognition in marine sector[END_REF], detecting people, cars, and roads [START_REF] Tseng | Combination of Computer Vision Detection and Segmentation for Autonomous Driving[END_REF], [START_REF] Soviany | Optimizing the Trade-off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction[END_REF] and detecting marine litter [START_REF] Fulton | Robotic detection of marine litter using deep visual detection models[END_REF].

1

2) Public datasets: One of the biggest problems of artificial intelligence, in general and image processing, is having a good dataset that properly relates to the problem. Besides that, the dataset has to be processed in a way that the model can make sense of the information. That way the model can successfully learn from that dataset. Scientific datasets are, at least, intermediate results in many scientific research projects. For some time, datasets were not even published and even if they were published it was mostly as a not re-usable by-product of the publication.

In maritime environment, we find public datasets VAIS (Visible and Infrared Spectrums) and Marvel [START_REF] Gundogdu | Marvel: A large-scale image dataset for maritime vessels[END_REF], used essentially for classification in [START_REF] Zhang | VAIS: A dataset for recognizing maritime imagery in the visible and infrared spectrums[END_REF], [START_REF] Redmon | Yolov3: An incremental improvement[END_REF] and [START_REF] Khellal | Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images[END_REF]. Details of the VAIS dataset, the most used dataset, are collected in Table I. Among popular datasets used for Image detection, we men-tion Pascal Visual Object Classes (Pascal VOC) [START_REF] Everingham | The Pascal visual object classes challenge: A retrospective[END_REF] dataset which contains 17 125 annotated images with 20 classes at the time of writing the article. This dataset was created within a challenge in visual object recognition funded by PASCAL network of excellence and then used to test and evaluate the performance of different models. However, there is not a public dataset annotated destined for fluvial environment with its different challenges that we detail below.

3) Object Detection in Maritime Environments: Target detection in maritime environments has a place to the investigate points that pick up small consideration in the field of computer vision. Some applications exist such as collision avoidance for autonomously operating vessels [START_REF] Statheros | Autonomous ship collision avoidance navigation concepts, technologies and techniques[END_REF] or object detection in general in [START_REF] Dilip | 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].However, these results can not be adapted to the fluvial environment since the two environments are different as we will detail in the following section.

III. DATASET AND GROUND TRUTH LABELLING PROCESS

The characteristics of the fluvial environment are different from those of the maritime environment. There are differences in natural features (rocks, waterfalls, rapids, etc). Seas are wider than rivers. So, the canal size is smaller that the waterway in the maritime environment. In addition, there are other differences related to the infrastructure. In fact, the river ships are smaller, the itineraries are more port-intensive and the ships are closer to the land. In addition, locks and bridges do not exist in the maritime environment and limit of the canal is not marked by a physical infrastructure, but generally with trees. Consequently, rules of inland navigation differ from the rules of maritime navigation. So, to evaluate the performances of Neural Networks, we need a specific dataset that meets these requirements.

Since no public dataset exists as described above, we make our own dataset 1 following a data model we propose and provide annotated images in two formats, as detailed below.

A. Data Model

The objects we aim to detect are divided in five categories: Riverside: As there is no lane markings in rivers, specifying the exact location of a lane marking in a camera image may be very ambiguous. So, in this category, we try to get as close as safely possible to the riverside, Vessel: In this category, we added different types of vessels that can be found in a river, Although following a single object is the most common object-following scenario, detecting multiple objects is neces-sary for our application. As shown in Fig. 1, we add multi-object detection capabilities by drawing multiple bounding boxes for multiple objects in the same image.

B. Dataset Construction

Our dataset is composed of 2 488 images. The number of annotations for each class is detailed in the Table II. In order to get close to real conditions, we selected images with different backgrounds, camera positions, weather conditions and day time. This allows us to create a dataset for training which closely conforms to real-world conditions, unlike previous works, which mostly rely on internally generated datasets with simulation software.

C. Annotation

We annotated our dataset using the LabelImg. This open source graphical image annotation tool can be downloaded on GitHub 2 . It was selected because it saved the annotation files to the PASCAL VOC format XML and annotation files are saved separately for each image in the source folder. So, it will be easier for future users of this dataset to reuse the annotations.

Unfortunately, right now there seems to be no standard on how to represent this information. In other words, each of the object detection systems expect a different format to represent the bounding box and class of each object:

Darknet uses a text file, with the following format:

IV. NETWORKS CONFIGURATION AND TRAINING

This section explains the neural network architectures se-lected, the pre-processing alterations done and the training process suggested.

A. Network Architectures

We choose for this project some network architectures from the most popular and successful object detection networks used today. Each one has its own benefits and drawbacks, with varying levels of accuracy and run-time speeds.

In this section, we will briefly discuss their methodologies and the related design choices.

1) Faster R-CNN: It is a project created by Shaoqing Ren, Kaiming He, Ross Girshick and Jian Sun [START_REF] Shaoqing Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF]. Similar to Fast R-CNN, the image is provided as an input to a convolutional network which provides a convolutional feature map. Instead of using selective search algorithm on the feature map to identify the region proposals, Faster R-CNN is a very accurate region-based deep detection model which improves Fast R-CNN by introducing the Region Proposal Networks. The predicted region proposals are then reshaped using a Region of interest (ROI) pooling layer which is then used to classify the image within the proposed region and predict the offset values for the bounding boxes.

2) Single Shot Multibox Detector (SSD):

It is a popular algorithm in object detection [START_REF] Liu | SSD: single shot multibox detector[END_REF]. It's generally faster than Faster RCNN. It uses some middle layers, called feature maps, in the network to detect objects of different sizes instead of predicting using the last layer, as done in traditional networks. Single deep learning network can significantly reduce computation time and improve inference accuracy.

3) Four versions of YOLO and Tiny-YOLO:

The You only look once (YOLO), first proposed in [START_REF] Redmon | You only look once: unified, real-time object detection[END_REF], is an object detection system targeted for real-time processing, which performs the region proposal and classification in one pass over the input image. It leads to a significant speed-up when compared to two-stage methods like Faster R-CNN. Two updates to the original YOLO method are described in [START_REF] Redmon | YOLO9000: Better, faster,stronger[END_REF], [START_REF] Redmon | Yolov3: An incremental improvement[END_REF] with some modifications to increase the detection precision and speed.

In this project, we use the improved versions of the models which are Yolo v2, Tiny-Yolo v2 [START_REF] Redmon | YOLO9000: Better, faster,stronger[END_REF], Yolo v3 and Tiny-Yolo v3 [START_REF] Redmon | Yolov3: An incremental improvement[END_REF].

B. Training Process

To train all the supervised deep models, we use an NVIDIA GeForce GTX 2080 Ti GPU with 11GB of GPU memory on a Linux machine (ubuntu 18.04). In addition we used CUDA Deep Neural Network library (cuDNN) which is a GPU-accelerated library created by NVIDIA to improve deep neural network performance. CuDNN provides optimized implementations for common routines such as forward and backward convolution, pooling, normalization, and activation layers. TensorFlow [START_REF] Abadi | Tensorow: Large-scale machine learning on heterogeneous distributed systems[END_REF] and Darknet [START_REF] Redmon | yolo[END_REF] libraries are used for implementation. Collecting labeled data is expensive. However, training a large neural network on a relatively small dataset, such as our dataset, would lead to over-fitting. So, to overcome this issue, we applied two methods:

Data augmentation: This technique has been shown to produce promising ways to increase the accuracy of detection tasks. We made some alterations to our existing dataset with rotation (angle=30 and angle=45) in order to increase the amount of training data. Neural network models consider additional as distinct images without concerning about the orientation.

Fine tuning: This technique means taking weights of a trained neural network and use it as initialization for a new model being trained on data from the same domain. It is used to both speed up the training and overcome small dataset size. In our implementations, we initialize our models with weights of the same trained models on Pascal VOC dataset.

Since the objects we aim to detect are different in term of size and shape, we calculated correctly tuned anchor boxes [START_REF] Yang | MetaAnchor: Learning to Detect Objects with Customized Anchors[END_REF] to improve the detection of irregular objects. We stop the training when the loss no longer decreases.

V. EVALUATION CRITERIA

We evaluate the different networks mentioned above using the following standard metrics: mAP (mean Average Precision): It is the first popular metric used for evaluating detection algorithms. Average precision computes the average precision value for recall value over 0 to 100. The mAP metric is the product of precision and recall of the detected bounding boxes. A higher value in mAP indicates a better performance. The mAP can be computed by calculating average pre-cision (AP) separately for each class, then calculating the average over the class. A detection is considered a true positive only if the Intersection over Union (IoU) is above 0.5. AP summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, using the increase in recall from the previous threshold as the weight:

∑ AP= n(Rn-Rn 1) Pn,
where Pn and Rn are the precision and recall at the n th threshold. IoU (Intersection over Union): It is the second popular metric used for evaluating detection algorithms. It is given by the ratio of the area of intersection and the area of union of the predicted bounding box and the ground truth bounding box.

IoU= AreaofOverlap AreaofUnion

These two metrics concisely describe the accuracy and the quality of object detections. Adding to that, we measure the runtime speed of the algorithms using a third metric. FPS (Frame per Second): It is the third important metric used for evaluating detection algorithms. It measures the number of frames processed by second.

VI. PERFORMANCE EVALUATION AND RESULTS ANALYSIS

Detection results are obtained on a test set, with images that do not appear in the train set. Our test set contains examples of every object mentioned in the data model with different background and camera position, to provide a realistic evalu-ation.

A. Metrics

To evaluate the performance of the models in terms of accuracy, we use the mAP. We set the IoU threshold to 0.5, as suggested in [START_REF] Cai | Cascade r-cnn: Delving into high quality object detection[END_REF]. If the classification of prediction matches the ground truth and IoU meets the threshold, the detection is considered as a correct prediction. Morevoer, to evaluate the performance of the models in terms of resource consumption, we use the FPS.

In Table III and Table IV, we illustrate the results obtained on the test set of the different models in terms of accuracy (mAP) and runtime speed (FPS). As we can see, all the six network architectures have similar behavior in detecting overall classes; the best predicted object is the vessel and the worst one is the riverside class. In Table III, we notice that YOLO and Tiny-YOLO have lower mAP when compared to Faster R-CNN and SSD. Conversely, Faster R-CNN and SSD have higher processing times, as shown in Table IV. In one hand, the Faster R-CNN model achieves much better detection performances compared to the other models although it is the slowest in terms of running time. In the other hand, YOLO v3 and SSD provide comparable detection performances. Although Tiny-YOLO provides fast running time, its detection performance is not as good as the other models. A model is said to be acting is real-time if its time response is less than the time response of a vessel. Then, to confirm whether the models are acting in real time conditions, we need to calculate the time a vessel make to stop, in real conditions.

The navigation authority responsible for the management of the inland waterways network [START_REF]Voies navigables de France[END_REF] indicates that "unless otherwise specified, signposts indicate limited speed on canals at 6 or 8 km/h and between 10 and 15 km/h in rivers". The type of ship, dimensions, mass and velocity are the parameters of greatest importance for the stopping distance and time of the ship. So, to calculate the minimum value of the stopping time (tmin), we should consider the maximum value of stopping distance (dmax) [START_REF] Sommet | Distances d'arrêl des grands navires en canal[END_REF]. dmax= (loadmax * speedmax 2 / (2* brake force) = 5.9 meters, tmin = dmax = 1.41 seconds vessels ′ sspeed 4

Hence, the results demonstrate that all the models, except the Faster R-CNN, validate their applicability in real-time object detection applications for inland navigation.

B. Prediction Results

In this section, we draw the predictions of the six models for the same two images. The result images reveal a correlation between detections in images and values in the Table III below. It is noticeable that all the models can detect almost all the classes with error percentages that differ from model to model and from image to image. By analyzing these results, we consider that Faster R-CNN is the best method from a standpoint of accuracy, but it requires much more resources than other models in terms of run-time speed. Tiny-YOLO v3 is the fastest method and Yolo v3 has the ideal balance of accuracy and run-time speed. Hence, we confirm that despite the differences, the selected state-of-art models perform as well as in other domain applications since we have achieved similar results.

C. Detection of Particular Objects: riverside

The six models show encouraging performance in general object detection. However, it still cannot identify and precisely localize particular objects, such as riverside in our case. As we notice in the Table III, the riverside class has the highest error values. In fact, this is due to the complexity of the class in terms of variations of its appearances, variations of the type of river, variations of the day time, for instance illumination appears to be totally different in the night, the presence of shadows of trees and vessels. Such situations are difficult even with human intervention.

In Fig. 8, we evaluate the detections predicted by the Tiny-YOLO v3 model, which has the worst performance in de-tecting the riverside class, on an image with only this class. We find some bounding boxes along the river, showing the edges. So, we conclude that even with this model, we can find useful results. Besides the results shown in Fig. 7, we confirm that these bounding boxes can be sufficient for the purpose of autonomous vessels, since it can detect almost all the near objects.

VII. CONCLUSION AND DISCUSSION

Real-time object detection plays a crucial role in autonomous vehicles as the information obtained using this concept can be used to direct the vehicle along the safest possible path by avoiding obstacles.

In this work, we addressed the challenges involved in real-time inland navigation. We exploited the existing deep convolutional neural network architectures, specifically, we tested Faster RCNN, YOLO v3, Tiny-YOLO v3, YOLO v2, Tiny-YOLO v2 and SSD to solve this problem. To accrately represent the specific needs of fluvial environment and since no dataset is available in litterature, we made a dataset of 2488 images with openly accessible for the community working on this area. We annotated it with a data model that contains five classes: vessel, person, riverside, road signals and infrastructure. The first comparative results demonstrate the effectiveness of these models in the fluvial domain.

In the future, in order to improve the performance of the detection, we consider increasing the size of the dataset by adding new annotated images and videos. In addition, we will apply a mathematical method to mark the edge of the river by a shape that accurately maps the navigation area.

  Person: We added this category to detect fishermen or other persons on the river's waters or persons in the riverside, since ships could be very close to the riverside, Infrastructure: In this category, we find two types of infrastructure that are locks and bridges, Road signals: As for cars as for boats, vehicles follow the traffic code by respecting the road signals. So, in this category we collect road signals we can find in real situations.Other versions of this data model were tested: one including a label for waves (rejected due to lack of data of this class and in consequence they present a lower accuracy) and models with multiple classes for different infrastructure and ships (rejected for lack of data on some classes and lower detection performance overall). Both versions achieved lower accuracy than this model on all networks. Examples of images annotated to have class-labels and bounding boxes from this data model can be seen in Fig.1.

Fig. 1 :

 1 Fig. 1: A sample from the training dataset are shown. Multiple rectangular bounding boxes are drawn to delimit objects with corresponding corresponding class labels.

[

  category number] [object center in X] [object center in Y] [object width in X] [object width in Y] Darkflow expects the annotations in the same format of the PASCAL VOC dataset in xml files with the following format: <width>width</width> <height>height</height> <depth>depth</depth> <bndbox> <xmin>xmin</xmin> <xmax>xmax</xmax> <ymax>ymax</ymax> </bndbox> Then we change XML files to text files using these equations: * Object center in X= (xmin+xmax)=2 width * Object center in Y= (ymin+ymax)=2 height * Object width in X= (xmax xmin)=2 width * Object width in Y= (ymax ymin)=2 height So, as we explained above, we build an annotated dataset where each image has two separate files (text file and XML file) in the two formats.

  2 https://github.com/tzutalin/labelImg

Fig. 5 ,

 5 Fig. 6, Fig. 7, Fig. 8, Fig. 9 and Fig. 10 show the predictions of Faster R-CNN model, YOLO v3 model, Tiny-YOLO v3 model, YOLO v2 model, Tiny-YOLO v2 and SSD model, respectively.

Fig. 2 :

 2 Fig. 2: A sample of predicted images where the rectangles and text overlaid on the figures are the outputs generated by YOLO v3 at test time.

Fig. 3 :

 3 Fig. 3: A sample of predicted images where the rectangles and text overlaid on the figures are the outputs generated by Tiny-YOLO v3 at test time.

Fig. 4 :

 4 Fig. 4: A sample of predicted images where the rectangles and text overlaid on the figures are the outputs generated by YOLO v2 at test time.

Fig. 5 :

 5 Fig. 5: A sample of predicted images where the rectangles and text overlaid on the figures are the outputs generated by Tiny-YOLO v2 at test time.

Fig. 6 :

 6 Fig. 6: A sample of predicted images where the rectangles and text overlaid on the figures are the outputs generated by Faster-RCNN at test time.

Fig. 7 :

 7 Fig. 7: A sample of predicted images where the rectangles and text overlaid on the figures are the outputs generated by SSD at test time.

Fig. 8 :

 8 Fig. 8: Detections generated by Tiny-YOLO v3 on a particular image with a complicate environment (shadow of trees).

TABLE I :

 I VAIS dataset.

	Total images IR Images Visible Images Night IR Images Number of classes	2865 1242 1623 154 6

TABLE II :

 II Proposed dataset.

	Total images Riverside Vessels Persons Infrastructure Road signals Number of classes	2488 28084 3496 1545 2051 193 5

TABLE III :

 III Detection metrics in mAP and AP.

	Model YOLO v3 Tiny-YOLO v3 45.42 45.24 mAP Person Riverside 60.36 64.87 27.53 12.69 YOLO v2 43.65 43.84 13.33 Tiny-YOLO v2 40.95 41.89 14.07 Faster R-CNN 72.14 76.2 49.67 SSD300 50.31 62.63 29.65	Vessel Infrastructure Road signals 80.16 68.08 61.14 68.65 52.36 48.19 65.36 38.59 54.13 60.98 43.61 44.20 92.96 76.35 65.52 56.9 56.15 46.21

TABLE IV :

 IV Detection metrics in FPS.

	Model YOLO v3 Tiny-YOLO v3 YOLO v2 Tiny-YOLO v2 Faster R-CNN SSD300	FPS 96 240 120 160 0.48 4.54

ACKNOWLEDGMENT

This work is achieved as part of a project partially funded by BFC region (Bourgogne-Franche-Comté).