In demersal trawl fisheries, the unavailability of the catch information until the end of the catching process is a drawback, leading to seabed impacts, bycatches and reducing the economic performance of the fisheries. The emergence of in-trawl cameras to observe catches in real-time can provide such information. This data needs to be processed in real-time to determine the catch compositions and rates, eventually improving sustainability and economic performance of the fisheries. In this study, a real-time underwater video processing system counting the Nephrops individuals entering the trawl has been developed using object detection and tracking methods on an edge device (NVIDIA Jetson AGX Orin). Seven state-of-the-art YOLO models were tested to discover the appropriate training settings and YOLO model. To achieve real-time processing and accurate counting simultaneously, four frame skipping ideas were evaluated. It has been shown that adaptive frame skipping approach, together with YOLOv8s model, can increase the processing speed up to 97.47 FPS while achieving correct count rate and F-score of 82.57% and 0.86, respectively. In conclusion, this system can improve the sustainability of the Nephrops directed trawl fishery by providing catch information in real-time.

在底层拖网渔业中，直到捕捞过程结束才可获得捕捞信息是一个缺点，会导致海底影响、兼捕并降低渔业的经济绩效。用于实时观察渔获量的拖网摄像机的出现可以提供此类信息。这些数据需要实时处理，以确定渔获量构成和比率，最终提高渔业的可持续性和经济绩效。在本研究中，使用边缘设备（NVIDIA Jetson AGX Orin）上的对象检测和跟踪方法开发了一种实时水下视频处理系统，用于对进入拖网的肾鱼个体进行计数。对七个最先进的 YOLO 模型进行了测试，以发现合适的训练设置和 YOLO 模型。为了同时实现实时处理和精确计数，评估了四种跳帧想法。研究表明，自适应跳帧方法与 YOLOv8s 模型相结合，可以将处理速度提高至 97.47 FPS，同时实现正确计数率和 F 分数分别为 82.57% 和 0.86。总之，该系统可以通过提供实时渔获信息来提高Nephrops定向拖网渔业的可持续性。