Work

Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea

Year: 2024

Type: article

Abstract: 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. • The YOLOv7 deep learning model was applied to diver-operated video (DOV) transects. • Manual and automated methods of identifying presence/absence were compared. • UVC and video data combined provides more complete site evaluation. • A total of 85 % of the species in situ were correctly identified by automation. • Epinephelus marginatus was twice as common inside of an MPA than outside.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2024.102959

Authors: Kilian Bürgi, Charles Bouveyron, Diane Lingrand, Benoit Derijard, Frédéric Precioso, Cécile Sabourault