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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0273588 |
| _version_ | 1811161641793880064 |
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| author | Azénor Le Quinio Eric De Oliveira Alexandre Girard Jean Guillard Jean-Marc Roussel Fabrice Zaoui François Martignac |
| author_facet | Azénor Le Quinio Eric De Oliveira Alexandre Girard Jean Guillard Jean-Marc Roussel Fabrice Zaoui François Martignac |
| author_sort | Azénor Le Quinio |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T06:18:47Z |
| format | Article |
| id | doaj.art-b39ff834316e4cd891c0eb186247463f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T06:18:47Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b39ff834316e4cd891c0eb186247463f2023-03-02T05:32:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01182e027358810.1371/journal.pone.0273588Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach.Azénor Le QuinioEric De OliveiraAlexandre GirardJean GuillardJean-Marc RousselFabrice ZaouiFrançois MartignacAcoustic 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.https://doi.org/10.1371/journal.pone.0273588 |
| spellingShingle | Azénor Le Quinio Eric De Oliveira Alexandre Girard Jean Guillard Jean-Marc Roussel Fabrice Zaoui François Martignac Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: Development of a cross-camera morphological analysis approach. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0273588 |
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