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Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach

Acoustic cameras are increasingly used in monitoring studies of diadromous fish populations, even though analyzing them is time-consuming. In complex in situ contexts, anguilliform fish may be especially difficult to identify automatically using acoustic camera data because the undulation of their b...

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Autores principales: Le Quinio, Azénor, De Oliveira, Eric, Girard, Alexandre, Guillard, Jean, Roussel, Jean-Marc, Zaoui, Fabrice, Martignac, François
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9956004/
https://www.ncbi.nlm.nih.gov/pubmed/36827318
http://dx.doi.org/10.1371/journal.pone.0273588
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author Le Quinio, Azénor
De Oliveira, Eric
Girard, Alexandre
Guillard, Jean
Roussel, Jean-Marc
Zaoui, Fabrice
Martignac, François
author_facet Le Quinio, Azénor
De Oliveira, Eric
Girard, Alexandre
Guillard, Jean
Roussel, Jean-Marc
Zaoui, Fabrice
Martignac, François
author_sort Le Quinio, Azénor
collection PubMed
description Acoustic cameras are increasingly used in monitoring studies of diadromous fish populations, even though analyzing them is time-consuming. In complex in situ contexts, anguilliform fish may be especially difficult to identify automatically using acoustic camera data because the undulation of their body frequently results in fragmented targets. Our study aimed to develop a method based on a succession of computer vision techniques, in order to automatically detect, identify and count anguilliform fish using data from multiple models of acoustic cameras. Indeed, several models of cameras, owning specific technical characteristics, are used to monitor fish populations, causing major differences in the recorded data shapes and resolutions. The method was applied to two large datasets recorded at two distinct monitoring sites with populations of European eels with different length distributions. The method yielded promising results for large eels, with more than 75% of eels automatically identified successfully using datasets from ARIS and BlueView cameras. However, only 42% of eels shorter than 60 cm were detected, with the best model performances observed for detection ranges of 4–9 m. Although improvements are required to compensate for fish-length limitations, our cross-camera method is promising for automatically detecting and counting large eels in long-term monitoring studies in complex environments.
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spelling pubmed-99560042023-02-25 Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach Le Quinio, Azénor De Oliveira, Eric Girard, Alexandre Guillard, Jean Roussel, Jean-Marc Zaoui, Fabrice Martignac, François PLoS One Research Article Acoustic cameras are increasingly used in monitoring studies of diadromous fish populations, even though analyzing them is time-consuming. In complex in situ contexts, anguilliform fish may be especially difficult to identify automatically using acoustic camera data because the undulation of their body frequently results in fragmented targets. Our study aimed to develop a method based on a succession of computer vision techniques, in order to automatically detect, identify and count anguilliform fish using data from multiple models of acoustic cameras. Indeed, several models of cameras, owning specific technical characteristics, are used to monitor fish populations, causing major differences in the recorded data shapes and resolutions. The method was applied to two large datasets recorded at two distinct monitoring sites with populations of European eels with different length distributions. The method yielded promising results for large eels, with more than 75% of eels automatically identified successfully using datasets from ARIS and BlueView cameras. However, only 42% of eels shorter than 60 cm were detected, with the best model performances observed for detection ranges of 4–9 m. Although improvements are required to compensate for fish-length limitations, our cross-camera method is promising for automatically detecting and counting large eels in long-term monitoring studies in complex environments. Public Library of Science 2023-02-24 /pmc/articles/PMC9956004/ /pubmed/36827318 http://dx.doi.org/10.1371/journal.pone.0273588 Text en © 2023 Le Quinio et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Le Quinio, Azénor
De Oliveira, Eric
Girard, Alexandre
Guillard, Jean
Roussel, Jean-Marc
Zaoui, Fabrice
Martignac, François
Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach
title Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach
title_full Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach
title_fullStr Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach
title_full_unstemmed Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach
title_short Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach
title_sort automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: development of a cross-camera morphological analysis approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9956004/
https://www.ncbi.nlm.nih.gov/pubmed/36827318
http://dx.doi.org/10.1371/journal.pone.0273588
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