TrapCounter: Integrating Detection, Tracking and Optical Flow for the Automatic Counting of Active Fishing Traps

Sablefish are a commercially important species in the northeast Pacific Ocean, typically caught using longline traps in a tightly regulated fishery. Mandatory electronic monitoring (EM) systems record deck activities to support compliance, but reviewing footage for trap hauling remains a manual, time-intensive process. This paper introduces TrapCounter, a method that automatically counts active fishing traps in EM videos by combining deep learning-based detection, tracking, and optical flow. We integrate the RetinaNet object detection architecture with the StrongSORT tracker to properly detect and track active traps as they are being hauled, addressing such challenges as varied poses and appearance due to the semi-transparent nature of traps and crowded deck environments. To reduce overcounting, we add an optical flow-based counting module that improves the tracker’s active trap count accuracy. We also release DTCFT, the first public dataset for trap detection, tracking, and counting, built from actual commercial and survey fishing vessel footage captured under varying conditions. Experiments show that TrapCounter outperforms approaches based on YOLOv9, Faster R-CNN, DeepSORT, and ByteTrack, streamlining EM workflows and enhancing EM scalability in commercial and survey fisheries. Dataset: https://github.com/alisoltani7596/TrapCounter <https://github.com/alisoltani7596/TrapCounter>