Automated detection, tracking and laser-based sizing of scallops from towed video using deep learning

Résumé fourni par la source

Fishery-independent video surveys are increasingly used to assess scallop populations due to their minimal environmental impact and high precision compared to traditional dredge methods. However, manual analysis of video footage is time-consuming and resource-intensive, limiting the efficiency and scale of survey analysis and delaying fishery management decision making. This study developed and evaluated deep learning models to automate scallop detection, counting, and sizing from underwater video surveys in Great Oyster Bay, Tasmania. We trained YOLOv8 object detection models on annotated datasets from 2021 and 2022 towed video surveys to identify live scallops, dead scallops, and other bivalves. Detections were linked across frames with BoT-SORT tracking so each individual was counted once. Separate models were trained to detect laser scaling points for calibration of size measurement. YOLOv8 models achieved scallop detection performance of mAP50 0.25–0.45 across all configurations, with live scallop detection reaching mAP50 0.54–0.75, while laser detection models achieved mAP50–95 > 0.81. Counting performance showed contrasting patterns between survey years: substantial undercounting in 2021 (19% of manual counts) due to limited training data, and closely matching counts in 2022 (100.4% of manual counts). Exploratory sizing analysis produced broadly similar size distributions (manual mean: 107.3 mm, SD: 18.1 mm; automated mean: 102.3 mm, SD: 23.3 mm), though a secondary cluster of small false positive detections inflated the sub-legal proportion in automated measurements. While this approach may not yet fully replace manual analysis, it demonstrates significant potential to transform scallop monitoring by enabling faster, scalable, and more consistent interpretation of underwater survey data.

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