Chahinez Bouguerche

Fadila Tazerouti

Delphine Gey

Jean-Lou Justine
email: justine@mnhn.frbouguerche

Fuentes Zambrano

983 (Monogenea: Gastrocotylidae) from Trachurus picturatus (Bowdich) (Perciformes: Carangidae) off the Algerian coast, Mediterranean Sea

come   L'archive ouverte pluridisciplinaire

Introduction

The Gastrocotylidae Price, 1943 was erected to separate Gastrocotyle Van Beneden & Hesse, 1863 by reference to a diagram of the clamp type alone (Price, 1943;[START_REF] Hargis | Monogenetic trematodes of Gulf of Mexico fishes. Part XII. The family Gastrocotylidae Price[END_REF]. [START_REF] Sproston | A synopsis of the monogenetic trematodes[END_REF] agreed on the importance of the difference in clamp structure in microcotylids but she reduced the Gastrocotylidae to subfamily status, as Gastrocotylinae included in the Microcotylidae Taschenberg, 1879 (see [START_REF] Sproston | A synopsis of the monogenetic trematodes[END_REF]. [START_REF] Palombi | I Trematodi d'Italia: parte I. Trematodi Monogenetici[END_REF] did not recognise Sproston's Gastrocotylinae and placed its members in the subfamily Microcotylinae Monticelli, 1892 in his own family Arreptocotylidae [START_REF] Palombi | I Trematodi d'Italia: parte I. Trematodi Monogenetici[END_REF] (see [START_REF] Palombi | I Trematodi d'Italia: parte I. Trematodi Monogenetici[END_REF]. [START_REF] Hargis | Monogenetic trematodes of Gulf of Mexico fishes. Part XII. The family Gastrocotylidae Price[END_REF] recognised the Gastrocotylidae with the subfamilies Gastrocotylinae [START_REF] Sproston | A synopsis of the monogenetic trematodes[END_REF]and Vallisiinae Price, 1943(see Hargis 1956); Gastrocotyle Van Beneden & Hesse, 1863 was designated as the type-genus [START_REF] Hargis | Monogenetic trematodes of Gulf of Mexico fishes. Part XII. The family Gastrocotylidae Price[END_REF]. [START_REF] Lebedev | Monogenea: suborder Gastrocotylinea[END_REF] recognised 16 genera in the family.

Currently, the Gastrocotylidae include about 20 genera (WoRMS, 2019), the most recently described being Allogastrocotyle [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF], erected to accommodate Allogastrocotyle bivaginalis [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF] from the gills of Trachurus lathami off Venezuela [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF]. This species has never been recorded since its original description.

In the course of a parasitological study of monogeneans of fishes off the southern coast of the Mediterranean Sea [START_REF] Chaabane | Pseudorhabdosynochus regius n. sp. (Monogenea, Diplectanidae) from the mottled grouper Mycteroperca rubra (Teleostei) in the Mediterranean Sea and Eastern Atlantic[END_REF](Chaabane et al. , 2016a(Chaabane et al. , b, 2017;;[START_REF] Kheddam | Hexabothriid monogeneans from the gills of deep-sea sharks off Algeria, with the description of Squalonchocotyle euzeti n. sp. (Hexabothriidae) from the kitefin shark Dalatias licha (Euselachii, Dalatiidae)[END_REF][START_REF] Ayadi | new species of Microcotyle (Monogenea: Microcotylidae) A new species of Microcotyle (Monogenea: Microcotylidae) from Scorpaena notata (Teleostei: Scorpaenidae) in the Mediterranean Sea[END_REF][START_REF] Derouiche | Holocephalocotyle monstrosae n. gen., n. sp. (Monogenea, Monocotylidae) from the olfactory rosette of the rabbit fish, Chimaera monstrosa (Holocephali, Chimaeridae) in deep water off Algeria[END_REF]Bouguerche et al., 2019a, b), we collected representatives of a monogenean similar to A. bivaginalis on the gills of the blue jack mackerel Trachurus picturatus (Bowdich). The specimens are described here and compared to the holotype of A. bivaginalis. Because their general body shapes are somewhat similar, we also compared sequences of our specimens with Pseudaxine trachuri Parona & Perugia, 1889 off the Algerian coast.

Materials and methods

Fishes

During 2016 and 2018, 241 specimens of Trachurus picturatus were collected off Zemmouri El Bahri (36°48ʹ 4.58ʺN, 3°34ʹ 7.01ʺE), Bouharoun (36ʹ370 24ʺ N, 2°39ʹ 17ʺ E) , Cherchell (36°36ʹ 31ʺ N, 2°11ʹ 50ʺ E), Dellys (36°54ʹ 48ʺN, 3°54ʹ 51ʺE) and Cap Djinet (36°52ʹ 37ʺ N, 3°43ʹ 23ʺE) off the Algerian coast. Fish specimens were transferred to the laboratory shortly after capture and identified using keys [START_REF] Fischer | Fiches FAO d'identification des espèces pour les besoins de la pêche[END_REF]. Gills were removed from each fish and were observed under microscope for the presence of monogeneans.

Monogeneans: morphological methods

Monogeneans were removed alive from gills using fine dissection needles, then fixed in 70% ethanol, stained with acetic carmine, dehydrated in ethanol series (70, 96 and 100%), cleared in clove oil, and mounted in Canada balsam. Drawings were made with the help of an Olympus BH-2 microscope with a drawing tube. Drawings were scanned and redrawn on a computer with Adobe Illustrator (CS5).

Measurements are in micrometres, and indicated as the range followed by the mean ± standard deviation if n ˃ 30 and the number of measurements in parentheses.

Monogeneans: molecular methods

To ensure that hosts and monogeneans were labelled with respect of host-parasites relationships we followed recent studies dealing with description of new species of Monogenea using barcoding [START_REF] Justine | The monogenean which lost its clamps[END_REF][START_REF] Ayadi | new species of Microcotyle (Monogenea: Microcotylidae) A new species of Microcotyle (Monogenea: Microcotylidae) from Scorpaena notata (Teleostei: Scorpaenidae) in the Mediterranean Sea[END_REF]Bouguerche et al., 2019a, b). For each host fish from which monogeneans were collected, a tissue sample from the gills was taken. The monogeneans were cut in three parts using a scalpel blade. Their anterior part (which includes the genital atrium) and posterior part (which includes the haptor) of each specimen were mounted as vouchers (both parts on a single slide) for drawing and deposition in a museum collection; their median parts were fixed in absolute ethanol then subjected to molecular analyses. Two specimens were analysed.

Molecular barcoding of fishes

Total genomic DNA was isolated using QIAamp DNA Mini Kit (Qiagen, Courtaboeuf, France) as per the manufacturer's instructions. The 5 ʹ region of the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene was amplified with the primers FishF1 (5 ʹ -TCA ACC AAC CAC AAA GAC ATT GGC AC-3 ʹ ) and FishR1 (5 ʹ -TAG ACT TCT GGG TGG CCA AAG AAT CA-3 ʹ ) [START_REF] Ward | DNA barcoding Australia's fish species[END_REF]. PCR reactions were performed in 20 µl, containing 1 ng of DNA, 19×CoralLoad PCR buffer, 3 mM MgCl 2 , 66 µM of each dNTP, 0.15 µM of each primer, and 0.5 units of Taq DNA polymerase (Qiagen). The amplification protocol was 4 min at 94°C, followed by 40 cycles at 94°C for 30 s, 48°C for 40 s, and 72°C for 50 s, with a final extension step at 72°C for 7 min. PCR products were purified (Ampure XP Kit, Beckman Coulter) and sequenced in both directions on a 3730xl DNA Analyzer 96-capillary sequencer (Applied Biosystems, Foster City, USA). We used CodonCode Aligner version 3.7.1 software (Codon Code Corporation, Dedham, MA, USA) to edit sequences, compared them to the GenBank database content with BLAST, and deposited them in GenBank under the accession numbers MN192395-MN192399 (Trachurus picturatus, 5 specimens) and MN192400-MN192401 (Trachurus trachurus, 2 specimens). Species identification was confirmed with the BOLD identification engine [START_REF] Ratnasingham | BOLD: The Barcode of Life Data System (www. barcodinglife. org)[END_REF].

cox1 sequences of monogeneans

Total genomic DNA was isolated using QIAmp DNA Micro Kit (Qiagen). The specific primers JB3 (=COIASmit1) (forward 5 ʹ -TTT TTT GGG CAT CCT GAG GTT TAT-3 ʹ ) and JB4.5 (=COI-ASmit2) (reverse 5 ʹ -TAA AGA AAG AAC ATA ATG AAA ATG-3 ʹ ) were used to amplify a fragment of 424 bp of the cox1 gene [START_REF] Bowles | A molecular phylogeny of the human schistosomes[END_REF][START_REF] Littlewood | Parasite speciation within or between host species? -Phylogenetic evidence from site-specific polystome monogeneans[END_REF]. PCR reaction was performed in 20 µl, containing 1 ng of DNA, 1× CoralLoad PCR buffer, 3 mM MgCl 2 , 0.25 mM dNTP, 0.15 µM of each primer, and 0.5 units of Taq DNA polymerase (Qiagen). Thermocycles consisted of an initial denaturation step at 94°C for 2 min, followed by 37 cycles of denaturation at 94°C for 30 s, annealing at 48°C for 40 s, and extension at 72°C for 50 s. The final extension was conducted at 72°C for 5 min. Sequences were edited with CodonCode Aligner software version 3.7.1 (CodonCode Corporation, Dedham, MA, USA), com-pared to the GenBank database content with BLAST, and deposited in GenBank under the accession numbers MN192391-MN192394.

Trees and distances

Most sequences of Gastrocotylidae available on GenBank were included in the phylogenetic analyses, but we did not use very short sequences or sequences without host identification. Two new sequence of Pseudaxine trachuri Parona & Perugia, 1889 from the type-host Trachurus trachurus off Algeria were also added. Sequences of two Gotocotylidae Yamaguti,1963 were used as the outgroup. The trees were inferred using the Neighbour-Joining (NJ) method and the Maximum Likelihood (ML) method using MEGA7 [START_REF] Kumar | MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets[END_REF]. For the latter, the best model, estimated in MEGA7, was the Hasegawa-Kishino-Yano model [START_REF] Hasegawa | Dating of the human-ape splitting by a molecular clock of mitochondrial DNA[END_REF] with discrete Gamma distribution (HKY + G). Genetic distances (p-distance and Kimura-2 parameter distance, [START_REF] Kimura | A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences[END_REF] were estimated with MEGA7.

All codon positions were used.

Results

Molecular identification of fish

The provisional identification of fish species using morphological characteristics was confirmed by DNA barcoding approach. BLAST analysis of the cox1 sequences of fish species of the present study with NCBI and BOLD database showed sequence similarity values of 99.69 % for Trachurus picturatus and 99.85% for Trachurus trachurus.

Molecular characterisation of monogeneans

The analysis involved 9 nucleotide sequences (Table 1). The cox1 sequences of A. bivaginalis were aligned with sequences of three other gastrocotylid species and two gotocotylids. The NJ and ML methods led to slightly different topologies and thus only the ML tree is shown (Fig. 1); these differences concerned only the relative position of the two basal species Pellonicola elongatus and Engraulicola thrissocles.

The Gastrocotylidae formed a monophyletic group separated from the outgroups. Within the gastrocotylid clade, a robust clade included all sequences of Pseudaxine trachuri [bootstrap support: 99% (NJ) and 97% (ML)]. Another clade, with high support, included our sequences of Allogastrocotyle bivaginalis (100% NJ and 99% ML). The P. trachuri and the A. bivaginalis clades were sister-groups (78% NJ and 79% ML).

Distances were computed using p-distance and Kimura-2 parameter (Tables 2,3). The differences were minor, and both are commented here. The two sequences of A. bivaginalis were identical. Distances between the sequences for A. bivaginalis and those for P. trachuri were 15-16% (p-distance) and 17-18% . This clearly demonstrates that the Allogastrocotyle species is distinct from P.

trachuri.

Family Gastrocotylidae Price, 1943 Genus Allogastrocotyle [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF] Allogastrocotyle bivaginalis [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF] Type-host: Trachurus lathami Nichols (Perciformes: Carangidae).

Other host: Trachurus picturatus (Bowdich) (Carangidae), blue jack mackerel (this paper).

Type-locality: Gulf of Cariaco, Venezuela.

Other localities: off Zemmouri el Bahri (36°48ʹ 4.58ʺ N, 3°34ʹ 7.01ʺE), Bouharoun (36°37ʹ 24ʺN,2°39ʹ 17ʺ E), Cherchell (36°36ʹ 31ʺN, 2°11ʹ 50ʺ E), Dellys (36°54ʹ 48ʺN, 3°54ʹ 51ʺE) and Cap Djinet (36°52ʹ 37ʺN, 3°43ʹ 23ʺE), off the Algerian coast (this paper).

Type-specimens: Holotype, USNPC 75594, USNM 1371132 (examined). Intestinal branches extending to posterior body extremity, obscured by staining. Testes post-ovarian, number indistinct; genital atrium armed with hooks, obliquely oriented; hooks indistinct. Atrial hooks as in Fig. 2, their number probably 12. Ovary pretesticular, folded, other details indistinct. Vaginal openings visible, ventrolateral in anterior narrowed part of body, slightly posterior to the genital atrium, anterior to bifurcation of oesophagus; vaginal openings unarmed, with irregular shape. Vaginal ducts not seen. One abnormal egg in uterus, elongated with two filaments, total length 430 (Fig. 2).

Remarks

The holotype is in good state but was unflattened and overstained, therefore obscuring most details of internal anatomy. In addition, most clamps are oriented laterally, precluding redescription of their structure.

Description of specimens from Trachurus picturatus off Algeria [Based on 32 specimens. Measurements in Table 4.] Body elongate, typically gastrocotylid, anterior extremity constricted. Haptor long, unilateral, asymmetrical, with single row of clamps. Clamps enclosed in clamp capsule, pedunculated; peduncles with short axial muscle bundles (Fig. 4C). Clamps typically gastrocotylid; ventral arm of median spring Y shaped, long and relatively narrow, proximal end of ventral arm of median spring very wide, its distal part Y-shaped, each limb abutting on short oblique sclerites, close to ventral jaw but not reaching it; dorsal arm of median spring wider than ventral, distally broad, with 3-4 pairs of apertures arranged in 2 longitudinal symmetrical parallel rows, distal end of dorsal arm with accessory skeletal piece represented by V-shaped process at its distal end (Fig. 6). Dorsal arm of ventral jaw short and curved inwards and almost reaching accessory skeletal piece of dorsal arm of median spring. Dorsal jaw shorter than ventral. Oblique sclerites longer, with inner ends touching each other along median line. Base of clamp with cuticularised hinge ligament on each side, connecting median spring with base of the jaw sclerites (Fig. 6).

Terminal lappet present (Fig. 4B), armed with 2 symmetrical pairs of gastrocotyloid anchors, dissimilar in shape and size: small, posteriormost anchor pair (Fig. 4E); large anteriormost anchor pair sickle-shaped and parallel (Fig. 4D). Pair of prohaptoral suckers subspherical, muscular, septate. Pharynx oval. Intestinal branches extending almost to posterior body extremity, not confluent, each branch with outer and inner ramifications (Fig. 4A).

Testes post-ovarian, relatively large, oval, arranged irregularly in posterior third of body and confined to intercrural field. Vas deferens runs forward along body midline; at level just anterior to genital atrium, swells to voluminous mid-ventral seminal vesicle (Fig. 4F,I), dorsal to uterus. Genital atrium ventral, with muscular rim, located in anterior restriction of body. Penis conical, slender, armed with circle of gastro-cotylid-like atrial hooks (Fig. 4F-I). Atrial hooks narrowing slightly at their distal ends (Fig. 4G) with bases of hooks with curved tips; ''roots'' of hooks extend sometimes over outer surface of proximal part of muscular genital atrium; shafts lie near penis, with curved, pointed tips directed inwards (Fig. 4F-H). In flattened whole-mounts, penis occasionally tilted, appears as pair of slender spines (Fig. 4F,I).

Ovary pretesticular in form of an inverted U. Ovary begins at level of anteriormost testes, continues anteriorly in midline, reflexes at level of confluence of vitelline ducts, and runs backwards toward haptor to end as oviduct. Oviduct arises from distal end of ovary and opens into ootype with Mehlis gland-cells.

Uterus, originating from ootype, runs forwards. Genito-intestinal canal short, dorsal to ootype . Vitellarium follicular, vitelline fields co-extensive with intestinal branches, extending from level of genital atrium to haptor. Transverse vitelloducts short and thick in some specimens, fusing in midline ventral to ovary, to form relatively large, Y-shaped vitelline reservoir (Fig. 5). Vaginal openings visible in all specimens, always paired, ventrolateral in anterior narrowed part of body, posterior to genital atrium; vaginal openings unarmed, with irregular shape (Fig. 4A). Vaginal ducts not seen.

Remarks

The species was described by [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF] based on an unknown number of specimens; measurements were given for only two, and a single specimen, designated as the holotype, Bouguerche et al.

Allogastrocotyle bivaginalis 10

was deposited in the United States Parasite Collection (now in the National Museum of Natural History, Washington, USA). Therefore, a single specimen is available, which we examined. As stated above, overstaining precludes any attempt at redescribing the internal soft anatomy of this unflattened specimen

Our specimens from T. picturatus from off Algeria are similar to the original description in the following characteristics body length body width haptor length number of testes, number of clamps, number of numberof atrial hooks number of anchors in the lappet. Most importantly, our specimens clearly show two vaginal openings, a feature mentioned in the original description and that we could verify on the holotype.

We could distinguish differences between our specimens and the original description in clamp structure.

In the original description, ends of limbs of the distal part of the ventral arm of the median spring articulate with the lateral marginal sclerite, in the drawing (figure 4 in [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF], the limbs touch the ventral jaws. In our specimens, the limbs abutt on the short oblique sclerites, close to the ventral jaw but not reaching it. In our specimens, an accessory skeletal piece represented by a projecting V-shaped process was observed on the distal end of the dorsal arm that was not mentioned nor illustrated in the type-material.

Some differences between the original description and our observations justify the need to emend the generic diagnosis: intestine branches are not confluent, and the ejaculatory bulb and penis were not described.

Allogastrocotyle Nasir & Fuentes Zambrano, 1983

Emended generic diagnosis Allogastrocotyle was originally described by [START_REF] Nasir | Algunos trema´todos monogene´ticos venezolanos[END_REF], to accommodate A.

bivaginalis from Trachurus lathami, off Venezuela, Western Atlantic. This genus was mainly characterised by having two ventro-lateral vaginal openings. The type-and only species remains poorly known and has never been recorded again; it has, however, been mentioned in lists [START_REF] Cohen | South American Monogenoidea parasites of Fishes, Amphibians and Reptiles: Published by the Authors Costa[END_REF][START_REF] Eiras | Parasitos de peixes marinhos da América do Sul[END_REF]Costa et al., 2017). Interestingly, some parasitological surveys on its host, T. lathami, did not record it [START_REF] Braicovich | Geo-graphical patterns of parasite infracommunities in the rough scad, Trachurus lathami Nichols, in the Southwestern Atlantic Ocean[END_REF][START_REF] Da Silva Gonc¸alves | Ecologia da comunidade de metazoa ´rios parasitos do xixarro, Trachurus lathami Nichols, 1920 (Osteichthyes: Carangidae) do litoral do Estado do Rio de Janeiro, Brasil[END_REF], suggesting it is rare.

Our specimens from T. picturatus in the southern Mediterranean unquestionably belong to the genus Allogastrocotyle, as shown by key features such as body shape and the number of vaginal openings.

We are aware of the existence of Pseudaxinoides [START_REF] Lebedev | Monogeneans of fish of the New Zealand-Australian shelf in the South China Sea (Monogenoidea: Gastrocotylidae, Gastrocotylinae)[END_REF], which has a similar body shape but, allegedly, multiple vaginal openings [START_REF] Lebedev | Monogeneans of fish of the New Zealand-Australian shelf in the South China Sea (Monogenoidea: Gastrocotylidae, Gastrocotylinae)[END_REF]. We examined specimens of monogeneans corresponding to the definition of Pseudaxinoides, confirm here that they are different from

Allogastrocotyle, but will discuss their taxonomic position in detail in a future paper. Our present molecular study showed that our specimens were different from Pseudaxine trachuri and thus support the validity of Allogastrocotyle (with two vaginal openings) vs Pseudaxine (no vaginal opening); however, we acknowledge that the number of species in the molecular analysis was low.

The question is whether our specimens from T. picturatus from Algeria are conspecific with A.

bivaginalis, described from another host (T. lathami) and from a distant location (off Venezuela, Western Atlantic). In Fig. 7, we mapped the distribution of both fish species, which do not overlap. A comparison of our specimens with the original description did show some differences in clamp structure, which could Bouguerche et al.

Allogastrocotyle bivaginalis 12

be used for erecting a new species; however, examination of the holotype of A. bivaginalis did not allow to confirm these differences. We did obtain molecular information for our specimens, but molecular information is lacking for A. bivaginalis from its type-locality. This situation is very similar to that encountered by Chaabane et al. (2016a) who found a species morphologically similar to the diplectanid monogenean Pseudorhabdosynochus sulamericanus [START_REF] Santos | Pseudorhabdosynochus spp. (Monogenea : Diplectanidae) from the gills of Epinephelus spp. in Brazilian waters[END_REF] in the Mediterranean; the hosts were different (Hyporthodus haifensis in the Mediterranean vs H. niveatus and H. nigritus in the Western Atlantic); morphological differences were subtle and did not allow to erect a new species; molecular information was available for the Mediterranean specimens but not for the specimens from the Western Atlantic [START_REF] Santos | Pseudorhabdosynochus spp. (Monogenea : Diplectanidae) from the gills of Epinephelus spp. in Brazilian waters[END_REF][START_REF] Kritsky | Pseudorhabdosynochus species (Monogenoidea, Diplectanidae) parasitizing groupers (Serranidae, Epinephelinae, Epinephelini) in the western Atlantic Ocean and adjacent waters, with descriptions of 13 new species[END_REF]Chaabane et al., 2016a). Chaabane et al. (2016a) concluded by ascribing their specimens to P. sulamericanus and discussed the problem of monogeneans which have been mentioned under the same binomial on both sides of the Atlantic; they remarked that no case of conspecific monogenean on both sides of the Atlantic had been demonstrated so far by both morphological and molecular methods, and this is still the case now.

Finally, due to overlapping in measurements and counts, we consider that the species of Allogastrocotyle from T. lathami and T. picturatus are conspecific, do not describe a new species, and record A. bivaginalis off Algeria, Mediterranean, on a new host, T. picturatus; this is the second record of this species, and of the genus, in the literature.

However, we believe that it is likely that future study will show that the species from the Mediterranean is a distinct species; this would require a detailed morphological and molecular study of specimens from the Western Atlantic.

  Specimens from Algeria: Vouchers deposited in the collections of the Muse ´um National d'Histoire Naturelle, Paris (MNHN HEL1091-HEL1092 and HEL1095-HEL1104) and in the USNM (1592618-1592621). Representative DNA sequences: cox1 (GenBank: MN192391, voucher MNHN HEL1091, and Gen-Bank: MN192392, voucher MNHN HEL1092, both from fish barcoded as GenBank MN192399). Vouchers represent anterior and posterior parts of specimens mounted together on slide, the median part used for molecular analysis. Site on host: Gills. Prevalence and intensity: Ex Trachurus picturatus off Algeria: 27% (57 out of 214 fish), up to 2 monogeneans/fish (this paper). Comparative material studied: Vouchers of Pseudaxine trachuri from the type-host, Trachurus trachurus, off Algeria, including two specimens with molecular information (MNHN HEL1093, GenBank: MN192393 and MNHN HEL1094, GenBank: MN192394, both from fish barcoded as GenBank MN192401). Redescription of the holotype (Fig. 2) [Measurements in Table 4.] Body elongate, typically gastrocotylid, anterior extremity constricted. Haptor long, unilateral, asymmetrical, armed with clamps arranged in single row, enclosed in clamp capsule supported by short peduncles which have axial muscle bundles. Number of clamps 32 (31 clamps present, anteriormost as broken peduncle). Structure of clamps indistinct, peduncle present. Terminal lappet present, armed with 2 pairs of anchors: small, posteriormost anchor pair and large anteriormost anchor pair; anterior anchors sickle-shaped and parallel. Pair of prohaptoral suckers sub-spherical, muscular, septate. Pharynx roughly pyramidal. Oesophagus bifurcating posterior to genital atrium.

Figure 1 .

 1 Figure 1. Molecular phylogenetic analysis by the Maximum Likelihood method based on the Hasegawa Kishino-Yano -model [1] of cox1 sequences of monogeneans. Bootstrap percentages (1,000 replicates) are indicated next to the branches when significant. There were a total of 299 positions in the final dataset. The Neighbour-Joining tree had similar topology and is not shown

Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .Figure 7 .

 234567 Figure 2. Holotype of Allogastrocotyle bivaginalis Nasir & Fuentes Zambrano, 1983 (USNM 1371132). A, Whole body; anteriormost clamp lacking; B, Anterior extremity showing relative positions of prohaptoral suckers, pharynx, genital atrium and lateral vaginal openings; C, Genital atrium (obliquely oriented); D, Posterior lappet

  Figure 1

Figure 7

 7 Figure 7

  Allogastrocotyle bivaginalis Nasir & Fuentes Zambrano, 1983 ex Trachurus lathami, Venezuela. Cemocotyle trachuri Dillon & Hargis, 1965 and Diplectanotrema trachuri Kovaleva, 1970. Three monogenean species were recorded from both fish species: Gastrocotyle trachuri van Beneden & Hesse, 1863, Pseudaxine trachuri and Heteraxinoides atlanticus Gaevskaya & Kovaleva, 1979.

	Bouguerche et al.	Allogastrocotyle bivaginalis	11
	of the Carangidae. Type-species: Discussion
	Costa et al. (2017) listed all parasites of fishes of the genus Trachurus. Monogeneans include: in T.
	lathami: A. bivaginalis and Pseudaxinoides cariacoensis Nasir & Fuentes Zambrano, 1983; in T.
	Testes post-ovarian, follicular; ejaculatory bulb present; genital atrium muscular, penis conical, armed
	with crown of hooks. Ovary near middle of body, shaped as inverted U. Vaginal opening double,
	ventrolateral, posterior to genital atrium. Genito-intestinal canal short, communicating with intestinal
	branch. Vitelline fields co-existing with intestine. Egg with polar filament on each pole. Parasites on gills

Gastrocotylidae, Gastrocotylinae. Body with slight depression at level of intestinal bifurcation. Haptor unilateral, asymmetrical, consisting of row of numerous clamps and terminal lappet. Clamps of Gastrocotyle-type, pedunculated; terminal lappet armed with two symmetrical pairs of anchors. Paired prohaptoral suckers muscular; pharynx oval, large; intestine with lateral diverticula, extend into haptor. picturatus:

Table 1 .

 1 Species used in the present molecular study. a New sequences; b Sequences published on

	GenBank only
	Parasite species	Host species	Origin	GenBank ID	Source
	Allogastrocotyle bivaginalis	Trachurus picturatus	Algeria	MN192391 a
	Allogastrocotyle bivaginalis	Trachurus picturatus	Algeria	MN192392 a
	Pellonicola elongatus Unnithan, 1967	Ilisha megaloptera	India	KU872043 b	Unpublished
	Engraulicola thrissocles (Tripathi, 1959) Lebedev, 1971	Thryssa hamiltonii	India	KU872046 b	Unpublished
	Pseudaxine trachuri	Trachurus trachurus	France	AY009168	Jovelin & Justine
	Parona & Perugia, 1889				(2001)
	Pseudaxine trachuri Parona & Perugia, 1889	Trachurus trachurus	Algeria	MN192393 a	Present study
	Pseudaxine trachuri Parona & Perugia, 1889	Trachurus trachurus	Algeria	MN192394 a	Present study
	Gotocotyla acanthura (Parona &	Scomberomorus	China	KF826085	Shi et al. (2014)
	Perugia, 1896) Meserve, 1938	niphonius
	Gotocotyla sawara Ishii, 1936	Scomberomorus	China	KF739594	Shi et al. (2014)
		niphonius
	.

Table 2 .

 2 Distances (in %) between taxa

		Sequence ID, species and host	1	2	3	4	5	6	7	8
	1	KF739594.1 Gotocotyla sawara ex Scomberomorus niphonius
	2	KF826085.1 Gotocotyla acanthura ex Scomberomorus niphonius	13
	3	MN192392 Allogastrocotyle bivaginalis ex Trachurus picturatus	25	26
	4	MN192391 Allogastrocotyle bivaginalis ex Trachurus picturatus	25	26	0
	5	MN192394 Pseudaxine trachuri ex Trachurus trachurus	26	25	18	18
	6	MN192393 Pseudaxine trachuri ex Trachurus trachurus	26	24	17	17	1
	7	AY009168.1 Pseudaxine trachuri ex Trachurus trachurus	26	25	18	18	0	1
	8	KU872046.1 Engraulicola thrissocles ex Thryssa hamiltonii	24	25	22	22	19	19	19
	9	KU872043.1 Pellonicola elongatus ex Ilisha megaloptera	27	23	23	23	22	22	22	20

Table 2 .

 2 Distances (in %) between taxa (p-distances).

		Sequence ID, species and host	1	2	3	4	5	6	7	8
	1	KF739594.1 Gotocotyla sawara ex Scomberomorus niphonius
	2	KF826085.1 Gotocotyla acanthura ex Scomberomorus niphonius	12
	3	MN192392 Allogastrocotyle bivaginalis ex Trachurus picturatus	21	22
	4	MN192391 Allogastrocotyle bivaginalis ex Trachurus picturatus	21	22	0
	5	MN192394 Pseudaxine trachuri ex Trachurus trachurus	22	21	16	16
	6	MN192393 Pseudaxine trachuri ex Trachurus trachurus	22	21	15	15	1
	7	AY009168.1 Pseudaxine trachuri ex Trachurus trachurus	22	21	16	16	0	1
	8	KU872046.1 Engraulicola thrissocles ex Thryssa hamiltonii	21	21	19	19	17	17	17
	9	KU872043.1 Pellonicola elongatus ex Ilisha megaloptera	22	20	20	20	19	19	19

Table 4 .

 4 Measurements of Allogastrocotyle bivaginalis from various hosts and localities.

	Hosts	Trachurus picturatus	Trachurus lathami	Trachurus lathami
	Locality	Off Algeria,	Off Venezuela, Atlantic Off Venezuela, Atlantic
		Mediterranean Sea
	No. of specimens	n = 33	2 specimens measured	Holotype
	Source	Present study	Nasir & Fuentes	Present study
			Zambrano (1983)
	Total body length	1,180-2,680 (2,030; n =	1,750-2,205	1,760
		26)
	Body width	310-1,930 (480; n = 26)	455-495	420
	Haptor length	310-1,870 (1,143; n =	945	945
		26)
	Lappet length		100-130
	Prohaptoral suckers	20-40 (29; n = 28)	16-26	20
	length
	Prohaptoral suckers width	15-50 (29; n = 28)	18-22	20
	Pharynx length	30-50 (38; n = 24)	54	30
	Pharynx width	20-52 (36; n = 24)	58	27
	Clamps number	23-36 (32 ± 3; n = 29)	32-33	32
	Clamp length	37-68 (55 ± 7; n = 48)	50-56	55
	Clamp width	31-68 (42 ± 6; n = 46)	41-44	33
	Small anchor	9-13 (11; n = 19)		13
	Long anchor	24 -32 (29; n = 20)	30-34	33
	Genital atrium length	25-50 (38 ± 6; n = 30)		37
	Genital atrium width	25-50 (39 ± 5; n = 30)		30
	Number of atrial hooks	11-13 (12; n = 20)	12	12
	Atrial hooks length	13-25 (21 ± 3; n = 41)	17-20	30
	Distance genital atrium-anterior end	125-310 (206; n = 27)	134-216	160
	Distance vagina-anterior end	180-480 (303; n = 26)		220
	Distance vagina-atrium	35-115 (74; n = 24)		25
	Egg length		80
	Egg width

Acknowledgements

We are greatly indebted to Anna Phillips from the United States National Museum, Washington, for kindly loaning the holotype. Professor Ken MacKenzie kindly provided literature. Our thanks are due to fishermen from Regaiia and especially Mohamed Kayrouze and Houcine. This work was funded by FSB, USTHB, Algeria, and ISYEB, MNHN, France.


Maria-Jesus Lobo
email: maria-jesus.lobo@enac.fr

Christophe Hurter
email: christophe.hurter@enac.fr

Mathieu Cousy
email: mathieu.cousy@enac.fr

A LIDAR Interactive Data Visualization for Ground Aircraft Detection at Small Airports

Keywords: Visualization, LIDAR, aircraft

Light Detection and Ranging (LIDAR) sensors are nowadays widely used for multiple applications such as autonomous driving, wind estimation and robots, however their use in airports has not yet been fully explored. Nevertheless, LIDAR sensors are decreasing in cost, and there are detection algorithms that could be useful in small airports. We present a system to visualize and detect aircraft in small airfields. The system enables an interactive calibration of the algorithm, and exploration of the results. The system has proven useful for the detection and positioning of airplanes in the ENVISION Sesar project.

I. INTRODUCTION

A-SMGCS (Advanced-Surface Movement Guidance and Control Systems) are used in airports to control and monitor aircraft and vehicles in order to optimize capacity, efficiency and safety [START_REF] Piazza | A-smgcs routing and guidance functions[END_REF]. They combine cooperative and non cooperative sensors. Their usage has grown in the last decade and they are widely used in airports control towers. However, their implementation is not currently available at small airports because of their high economic cost.

The ENVISION SESAR project aimed at implementing a low cost surveillance system using new low cost technologies and the advances in detection algorithms, such as surveillance cameras and LIDAR sensors. The advances in image processing techniques make it possible nowadays to achieve real time object detection in video streams. LIDAR sensors have been successfully used in a wide set of applications, such as autonomous driving and forest classification and their cost is decreasing. To take advantage of both technologies, a system consisting of an array of cameras, ADS-B (Automatic Dependent Surveillance-Broadcast) receptors and a LIDAR sensor was studied in two different airports. The fusion of these data is displayed to the controller in an Airport Operator A-CDM user interface. The preliminary results show that the proposed system can achieve detection of aircraft, vehicles and persons at approximately 400 meters, 200 meters and 100 meters respectively.

The system relies on machine learning techniques to classify objects in the video stream and on a segmentation algorithm [START_REF] Bogoslavskyi | Fast range image-based segmentation of sparse 3d laser scans for online operation[END_REF] to detect objects in the LIDAR data. The LIDAR algorithm is complemented by a calibration phase to correctly filter out noise, such as very small objects or false detections. To ease this phase, we present a web-based interactive visualization system that displays the point cloud and the detected objects by the algorithm, and enables tunning the algorithm parameters interactively.

This paper focus on the LIDAR contribution to the project and is organized as follows. First, we present the related work that studies the use of LIDAR sensors in airports, the visualization techniques for 3D point clouds and an overview of existing systems. Then, we give an overview of the EN-VISION project. Then, we present the implemented system and features. Finally, because one of the main advantages of using a LIDAR sensor is its capability to work in low visibility conditions, we present the results obtained during a field test in light rainy weather. The results suggest that the LIDAR detection algorithm provides high precision positioning for a vehicle in this scenario. We finish with a discussion about the limitations of the system and some perspectives for future work.

II. RELATED WORK

A. Lidar usage in airports

The usage of LIDAR sensors in airports has gained interest recently. For example, Toth et al. [START_REF] Toth | Monitoring aircraft motion at airports by lidar[END_REF], [START_REF] Koppanyi | Object tracking with lidar: Monitoring taxiing and landing aircraft[END_REF] use four Velodyne VLP-16 sensors to detect aircraft in the taxiing lane, resulting in motion and position estimation. Mund et al. [START_REF] Mund | Performance evaluation of lidar point clouds towards automated fod detection on airport aprons[END_REF] explored the use of a LIDAR sensor to detect foreign objects on airport aprons. They were focused on detecting small objects that might be hazardous, using a high resolution LIDAR. Their results suggest that the size of the reflective area of the target objects highly influences the resulting detection, and that reflectivity might be more important than distance for object detection. They achieved good detection rates for medium and big size standardized objects.

Our approach is similar to the one presented by Hoch et al. [START_REF] Toth | Monitoring aircraft motion at airports by lidar[END_REF], but instead of using multiple LIDAR sensors, we use only one as we focus on detecting only aircraft position, and not trajectory. Also, the configuration of the system is different because the goal is to reproduce the ground radar information, and not to calculate precise trajectories. Furthermore, the contribution of this paper lies more in the interactive visualization of the point cloud, than in the detection algorithm and configuration of the system.

B. Point cloud visualization

Visualizing large point clouds, such as the ones produced by LIDAR sensors, to gather terrain data is challenging because of the big number of points captured. Several systems have been proposed to visualize LIDAR data [START_REF] Kuder | Web-based lidar visualization with point-based rendering[END_REF], [START_REF] Maravelakis | 3dsystek web-based point cloud viewer[END_REF], [START_REF] Kulawiak | Application of web-gis for dissemination and 3d visualization of large-volume lidar data[END_REF]. Kuder et al. [START_REF] Kuder | Web-based lidar visualization with point-based rendering[END_REF] propose a web based system that enables users to retrieve LIDAR point clouds remotely and visualize them through point based rendering. Maravelakis et al. [START_REF] Maravelakis | 3dsystek web-based point cloud viewer[END_REF] propose also a web based system to display and navigate in 3D point clouds. A more recent approach, proposed by Kuder et al. [START_REF] Kuder | Point-based rendering optimization with textured meshes for fast lidar visualization[END_REF], create textures from LIDAR data and use them to render the final surfaces, achieving a faster visualization. Deibe et al. [START_REF] Deibe | Gvlidar: an interactive web-based visualization framework to support geospatial measures on lidar data[END_REF] propose a web based Lidar Visualization and they provide tools to make geospatial measurements as well.

These works focus mostly on efficient visualizations because they are targeted to display big point clouds. In our case, the point clouds generated by the LIDAR, are sparse, so using traditional rendering techniques is enough. Our system rather focuses on performing the visualization and detection in real time, and providing visual cues to help understanding the algorithm results.

There are some commercial and open source existing software to visualize LIDAR data in real time, such as LAStools [11], Fugro Viewer [12], Lidar View [START_REF] Krivutsenko | Lidar view[END_REF], Potree [START_REF] Schütz | Potree: Rendering large point clouds in web browsers[END_REF] and Veloview [START_REF] Paraview | [END_REF]. VeloView, for example, is a multi-plataform solution that can be connected to a Velodyne LIDAR and provide a visual representation of the live cloud point. It provides several color encodings for the points, and some basic filtering operations, such as cropping the data on the x, y and z axis. It also supports some basic navigation such as panning and zooming. The system described in this paper also provides these options, but it adds the capability to apply real time detection to the data and visualize different algorithm outputs.

C. Interaction techniques

Interaction techniques further enhance these visualization, by making it possible for the users to navigate and select subsets of the data. Selection techniques for 3D visualization have been widely investigated, such as ray casting [START_REF] Sangyoon Lee | Evaluation of pointing techniques for ray casting selection in virtual environments[END_REF], alternative spaces [START_REF] Hurter | Color tunneling: Interactive exploration and selection in volumetric datasets[END_REF] and 3D volumes [START_REF] Yu | Efficient structureaware selection techniques for 3d point cloud visualizations with 2dof input[END_REF]. Some techniques leverage the data context and characteristics to facilitate selection and exploration. The CAST techniques are designed specifically for selection in 3D point clouds and use context information to infer the desired user selection. Prouzeau et al. [START_REF] Prouzeau | Scaptics and highlight-planes: Immersive interaction techniques for finding occluded features in 3d scatterplots[END_REF] introduce new techniques for scatterplot exploration that maps the data density to the vibration of virtual reality controllers, providing a haptic feedback.

Immersive technologies have also been used to visualize LIDAR data. Burwell et al. [START_REF] Burwell | The potential for using 3d visualization for data exploration, error correction and analysis of lidar point clouds[END_REF] propose an virtual reality environment to explore LIDAR data. An evaluation with expert users suggest that this visualization can be useful to have a more realistic perception of the data. Ave [START_REF] Neumann | Augmented virtual environments (ave): Dynamic fusion of imagery and 3d models[END_REF] uses LIDAR data to extract buildings and then project imagery on top of them to create a virtual environment.

III. ENVISION

A. Overview

The ENVISION system couples multiple sensors and processing modules to calculate the position of aircraft, vehicles and people. For now, the ENVISION system uses multiple cameras, one LIDAR and one ADS-B sensor. Each one of these sensor feeds a different processing module, that decode and process its data. The processing results are then consolidated in a Data Fusion module that track targets, calculate target positions and generate aircraft movement events. The platform overview is displayed in Figure 1.

The ENVISION project was tested on Muret Airfield, France. The LIDAR was positioned on a mast at a height of approximately 8 meters. From there, it covered part of the parking area and the closest taxiing.

B. LIDAR processing

The LIDAR processing module is in charge of processing the raw data coming from the sensor and performing real time object detection in the point cloud. The LIDAR sensor measures the distance to objects by using laser lights. In our case, the LIDAR generates point clouds, where each point is characterized by its distance, and its horizontal and vertical angles. This is then used to calculate the point positions in 3D. To perform object detection, we use the algorithm presented by Bogoslavsky and Stachniss [START_REF] Bogoslavskyi | Fast range image-based segmentation of sparse 3d laser scans for online operation[END_REF]. This algorithm is specially To achieve fast segmentation, Bogoslavsky and Stachniss propose to perform the detection on the 2D image generated by the LIDAR rather than in the 3D point cloud. For this, the algorithm starts by performing a classification to ignore the points that are on the ground. In our case, we use a plane fitting approach. Then, for each point that is not in the ground, four neighbors are considered: the two closest points coming from the same laser (horizontal neighbors) and the closest points with the same azimuth coming from the top and down lasers (vertical neighbors). With this, it is possible to build a graph where each point will have a maximum of four neighbors, depending of the distance between the points. For two points to be considered neighbors, the distance between them has to be inferior to a distance threshold.

Once the graph is built, for each two neighbors, the beta angle is calculated, as the angle between the segments that goes through the LIDAR origin and the farthest point among the two neighbors, and the line going through the two points. This angle reflects the depth difference between the two points, if it is small the points are probably at different depths and thus probably belong to different objects. A beta threshold is determined to decide if two points belong to the same object or not.

Because this algorithm requires a calibration of the beta threshold and the distance threshold parameters, we designed and implemented an interactive visualization that enables users to calibrate the algorithm. This system is described in the following section.

IV. INTERACTIVE VISUALIZATION

The system was designed to support the design and implementation of the LIDAR detection algorithm. Because of this, we draw four design requirements to enable users to explore the LIDAR data and the algorithm results. These requirements are inspired by existing visualization systems.

A. Design requirements

• Support navigation in the point cloud through basic operations like pan and zoom. • Support manipulation of the algorithm parameters and present the results directly in the visualization • Support exploration of live and recorded data • Present visually cues to enable users to understand how the algorithm work

B. Features

The final interface is depicted in Figure 2, and consists of an inspector, so that the user can calibrate the algorithm, and a point cloud visualization, so that the user can explore the data and the algorithm results. The point cloud visualization supports panning and zooming operations. Through the inspector, the user can tune the algorithm calibration and the visualization. The specific features addressing the design requirements are detailed next. 1) LIDAR calibration: Because the fusion module used in the ENVISION solution requires having absolute positions in (lat, lon) of the detected objects, the positions found in the LI-DAR coordinates need to be transformed to world coordinates. This requires knowing the LIDAR geographical position and its orientation. We propose using a geo-referenced image to enable the user to infer the LIDAR position and orientation by matching the cloud point to the image. To achieve this, he can set the position and orientation in the inspector and the changes will be reflected in the interface, so he can visually match a landmark visible in both representations. Figure 2 depicts how the user can match the roof lines visible in the LIDAR point cloud to the roof on the image. The final transformation to world coordinates is done using the Turf library [START_REF] Turfjs | [END_REF].

2) Algorithm interactive parameter setting: Using the inspector, the user can also select the information coded through the points' colors. The available parameters are height of the points, distance from the LIDAR, received intensity, azimuth and vertical laser id. These parameters match the ones available in the existing visualization solutions, such as VeloView [START_REF] Paraview | [END_REF].

The user can also select to depict the results of the detection algorithm, as illustrated in Figure 3. Because the results of the algorithm depend on the correct classification of points as ground or non ground, we use the color to depict this classification. Points classified as ground are depicted in green, and non ground in blue.

The detected objects are depicted as white bounding boxes. Using the inspector, the user can set the distance threshold and the beta threshold parameters and see the changes in the detection results immediately. We also incorporated some additional parameters that were proven useful to further reduce false detections:

• Minimun distance from the LIDAR: This helps reducing false detection due to very close objects, such as the cameras in the mast. • Minimum and maximum size of the objects: Because we were interested in detecting planes, we set a minimum and maximum size to reduce false detections. This comes at the cost of potentially missing objects, if they are covered by few points. The limitations will be discussed in more detail in the Section VI.

• Minimum and maximum azimuth: As depicted in Figure 3, some objects are detected on the building roof. To avoid such detections, we can filter the minimum and maximum azimuths so that only the detections happening in the airfield parking are considered. Each selected object can also be selected and removed from the detected objects. This will result in filtering out all the objects at a vicinity of the removed object. This is useful to remove recurrent false detections produced by noise.

C. Interactive exploration of the algorithms' results

In order to make the algorithm processing more understandable, we first propose to enable the exploration of the graph calculated by the algorithm described in Section III. Some visualization techniques use propagation to explore graphs, by extending node selection to their neighbors ( [START_REF] Heer | Vizster: visualizing online social networks[END_REF], [START_REF] Mcguffin | Interaction techniques for selecting and manipulating subgraphs in network visualizations[END_REF], [START_REF] Prouzeau | Evaluating multi-user selection for exploring graph topology on wall-displays[END_REF]). We attempt to apply this concept to explore the algorithm results. The user can select any point classified as non ground, and its neighbors will be highlighted in orange and the links between them displayed with a yellow line.

This might help to understand the algorithm results and to better tune the algorithm parameters. For example, in Figure 4-(a) only a part of an object is classified as such. By clicking one of the points in the border of the object, we can see that the two points are neighbors, so they are probably not considered part of the same object because of the distance threshold parameter. By setting its value to 4 meters instead of 2 meters, the two objects are considered as one.

Because many changes might happen from one frame to another, it might be useful to provide to the user an overview of where the point cloud is changing, and what is the direction of the change. To achieve this, we propose to calculate a flow field that will represent the movement from one frame to another, similar to what the Optical Flow [START_REF] Horn | Determining optical flow[END_REF] algorithm does. For each point in the 2D image (azimuth and vertical angle), we use a set of neighbors to find a matching set of neighbors of the same size in the following frame, by finding the points that present the smaller difference in terms of distance from the LIDAR. Then, the vector field will display the direction from the initial point to the matched point. Figure 5 depicts such flow. We are currently investigating other techniques to visualize this data, such as flow visualization techniques. Figure 6 depicts such flow depicted using the OLIC [START_REF] Wegenkittl | Animating flow fields: rendering of oriented line integral convolution[END_REF] technique. This technique depicts direction as animated droplets, that are brighter following the flow direction.

D. Implementation

The system is implemented through two main components: a server and a client. The server is in charge of retrieving the point cloud (either from the database or from the sensor), processing it and running the detection algorithm. The server is based on the Node.js1 technology. The web client is based on the libraries Three.js2 , used for the 3D visualization, and React.js3 to handle user interaction. We chose Three.js because it is a powerful toolkit to display 3D graphics that eases the use of WebGL. The system is able to perform the rendering and detection of a point cloud once per second. When performing real time detection on the airport, only the detection algorithm runs, detecting at 5 frames per second.

We use a LIDAR Velodyne VLP-16. This LIDAR has a horizontal resolution of 360 and 16 vertical angles. All the displayed images were taken on the Muret Airfield, France. The LIDAR is positioned on a mast in the airfield at a height of approximately 8 meters. Because the LIDAR is on the mast, we only can use approximately half of the 16 lines produced by the sensor.

V. EVALUATION

In order to evaluate the performance and accuracy of the LIDAR detection algorithm, we conducted a test on the Muret airfield where a car equipped by a GPS drove around the parking under light rainy weather. For the 400 seconds of the recording, the LIDAR detected the car 85% of the time, with an accuracy of 6.7m 50% of the time and 10.2m 95% of the time, as depicted in Figure 8. The accuracy does not seem to depend on the distance, as depicted in Figure 9 However, the detection presents six gaps of 3s where the car is not detected. These gaps are visible in Figure 7. The gaps can be explained because we did not have points that covered the car in those moments.

VI. DISCUSSION AND CONCLUSION

We described a LIDAR interactive visualization system to ease the calibration phase and explore the results of a detection algorithm. The algorithm achieved good results under light rain weather. However, our approach presents several limitations. First, we have only conducted one validation in a specific context. The results are encouraging but more tests are needed to completely assess the system and detection.

Second, the tuning of the algorithm parameters and the filtering of the results can be useful to avoid false detections. However, this comes at the cost of missing objects in the detection phase. For example, we cannot detect persons or small vehicles, because we filter out object that have a bounding box where the longest diagonal is smaller than 1m. During preliminary tests, we observed that not setting this minimum size produced too many false detections, thus we decided to limit the LIDAR to airplane detection.

Third, our algorithm does not perform any kind of classification, thus we cannot know what kind of object has been detected. However, this does not hamper the ENVI-SION platform, because the LIDAR and the video data are complementary. The LIDAR detection provides precise depth measurements, while the video processing results in accurate classification.

These two latter limitations could be mitigated by increasing the resolution of the point cloud. This could be easily achieved by using a LIDAR with higher resolution. However, this would increase the cost of the system. Increasing the resolution could also be possible using the current LIDAR. First, the sensor could be oriented towards the floor, thus increasing the coverage of the area close to the mast. Second, we could make the LIDAR oscillate in order to make it cover the gaps between two lasers.

Figure 1 .

 1 Figure 1. Overview of the ENVISION platform. Sensors are depicted in blue, the processing units in purple, outputs in green.

2 9

 2 th SESAR Innovation Days 2 nd -5 th December 2019 ISSN 0770-1268 b a Alignment of the border of the building with the roof lines visible in the point cloud

Figure 2 .

 2 Figure 2. Interface of the system. (a) Inspector area (b) Point cloud visualization. The background image can be used to find the rotation and position of the LIDAR.

Figure 3 .

 3 Figure 3. Detection results using the algorithm presented in [2], depicted as white wireframe boxes.

Figure 4 .

 4 Figure 4. Example of the exploration of the algorithm results.Clicking on one node will display its neighbors as visible in (a). The distance between nodes to be considered neighbors can be modified so the identified objects are grouped as one.

Figure 5 .

 5 Figure 5. Example of a flow generated using a 2D image. Here each laser is represented as a horizontal line, and the x coordinate represents the azimuth. The green arrows show the direction of the change between the depicted frame and the next one. Here we only display the lines covering the parking area

Figure 7 .

 7 Figure 7.Points recorded by the GPS in the car (blue), and positions detected by the LIDAR algorithm (yellow). The missing detection on the left of the image can be explained by a lack of points on the car at those times. Background image just for reference.

Figure 8 .Figure 9 .

 89 Figure 8. Histogram of LIDAR detection accuracy

th SESAR Innovation Days 2 nd -5 th December 2019 ISSN 0770-1268

https://nodejs.org/en/

https://threejs.org/

https://reactjs.org/

th SESAR Innovation Days

ACKNOWLEDGMENT

This research was partly supported by SESAR H2020 project ENVISION grant agreement No 783270. 2 nd -5 th December 2019 ISSN 0770-1268