Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea
Assessing underwater biodiversity is labour-intensive and costly, but is crucial for measuring the extent of the decline in local fish stock. In most cases, Underwater Visual Census (UVC) is the preferred method, however this can be costly in terms of human effort and is limited by meteorological an...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124005016 |
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| author | Kilian Bürgi Charles Bouveyron Diane Lingrand Benoit Derijard Frédéric Precioso Cécile Sabourault |
| author_facet | Kilian Bürgi Charles Bouveyron Diane Lingrand Benoit Derijard Frédéric Precioso Cécile Sabourault |
| author_sort | Kilian Bürgi |
| collection | DOAJ |
| description | Assessing underwater biodiversity is labour-intensive and costly, but is crucial for measuring the extent of the decline in local fish stock. In most cases, Underwater Visual Census (UVC) is the preferred method, however this can be costly in terms of human effort and is limited by meteorological and logistical factors. Advances in technology allows the utilisation of more autonomous video recording methods (i.e. Remote Operated Vehicles (ROV)) which addresses these limitations. This study used a transect-wise UVC coupled with diver operated videos (DOV). For the video analysis, a comprehensive fully automated pipeline was developed to extract frames from DOV and perform colour correction. This pipeline integrates a YOLO-based model to detect 20 Mediterranean fish species and validate the presence or absence of each species within individual transects. This study was conducted to evaluate the feasibility of using video-based methods for UVC with minimal human-input. The result of automated video analysis were in agreement with manual video counting, validating the autonomous and bias-free procedure for video assessment. In conclusion, utilising a minimal-human-input video method liberates the data acquisition from limiting factors (i.e. meteorological and logistical) and automation of this video analysis significantly reduces the labour and time required. For future fieldwork campaigns, the video data collection protocol needs to be modified to better resemble traditional UVC and enhance this acquisition method. |
| format | Article |
| id | doaj-art-40ce5ed4a41949a2ab528a889cce4685 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-40ce5ed4a41949a2ab528a889cce46852025-01-19T06:24:39ZengElsevierEcological Informatics1574-95412025-03-0185102959Towards a fully automated underwater census for fish assemblages in the Mediterranean SeaKilian Bürgi0Charles Bouveyron1Diane Lingrand2Benoit Derijard3Frédéric Precioso4Cécile Sabourault5Université Côte d'Azur, CNRS, ECOSEAS, Nice, France; Université Côte d'Azur, Inria, CNRS, Laboratoire J.A.Dieudonné, Maasai team, Nice, FranceUniversité Côte d'Azur, Inria, CNRS, Laboratoire J.A.Dieudonné, Maasai team, Nice, FranceUniversité Côte d'Azur, Inria, CNRS, I3S, Maasai team, Nice, FranceUniversité Côte d'Azur, CNRS, ECOSEAS, Nice, FranceUniversité Côte d'Azur, Inria, CNRS, I3S, Maasai team, Nice, FranceUniversité Côte d'Azur, CNRS, ECOSEAS, Nice, France; Corresponding author.Assessing underwater biodiversity is labour-intensive and costly, but is crucial for measuring the extent of the decline in local fish stock. In most cases, Underwater Visual Census (UVC) is the preferred method, however this can be costly in terms of human effort and is limited by meteorological and logistical factors. Advances in technology allows the utilisation of more autonomous video recording methods (i.e. Remote Operated Vehicles (ROV)) which addresses these limitations. This study used a transect-wise UVC coupled with diver operated videos (DOV). For the video analysis, a comprehensive fully automated pipeline was developed to extract frames from DOV and perform colour correction. This pipeline integrates a YOLO-based model to detect 20 Mediterranean fish species and validate the presence or absence of each species within individual transects. This study was conducted to evaluate the feasibility of using video-based methods for UVC with minimal human-input. The result of automated video analysis were in agreement with manual video counting, validating the autonomous and bias-free procedure for video assessment. In conclusion, utilising a minimal-human-input video method liberates the data acquisition from limiting factors (i.e. meteorological and logistical) and automation of this video analysis significantly reduces the labour and time required. For future fieldwork campaigns, the video data collection protocol needs to be modified to better resemble traditional UVC and enhance this acquisition method.http://www.sciencedirect.com/science/article/pii/S1574954124005016Diver operated videoAutomated UVCDeep learningObject detectionMarine biologyMarine protected areas |
| spellingShingle | Kilian Bürgi Charles Bouveyron Diane Lingrand Benoit Derijard Frédéric Precioso Cécile Sabourault Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea Ecological Informatics Diver operated video Automated UVC Deep learning Object detection Marine biology Marine protected areas |
| title | Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea |
| title_full | Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea |
| title_fullStr | Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea |
| title_full_unstemmed | Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea |
| title_short | Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea |
| title_sort | towards a fully automated underwater census for fish assemblages in the mediterranean sea |
| topic | Diver operated video Automated UVC Deep learning Object detection Marine biology Marine protected areas |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124005016 |
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