Application of an electronic monitoring system for video target detection in tuna longline fishing based on YOLOV5 deep learning model
WANG Shuxian, ZHANG Shengmao, ZHU Wenbin, SUN Yongwen, YANG Yuhao, SUI Jianghua, SHEN Lie, SHEN Jieran
1.College of Navigation and Ship Engineering, Dalian Ocean University, Dalian 116023, China; 2.Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs，East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; 3.Key Laboratory of Sustainable Utilization of Technology Research for Fishery Resource of Zhejiang Province, Marine Fisheries Research Institute of Zhejiang Province, Zhoushan 316021, China; 4.Liancheng Overseas Fishery(Shenzhen) Company Limited, Shenzhen 518035, China
Abstract: In order to evaluate the operation quality of the tuna longline fishing system, reduce labor costs, and extract information such as float and tuna quantity from the electronic monitoring system of the tuna longline fishing system, a method for detecting floating ball and tuna target in tuna longline fishing electronic monitoring system was proposed based on deep learning YOLOV5 network model. A total of 15 578 key frames containing target float or tuna were intercepted from the video surveillance data of the HNY722 ocean-going fishing vessel EMS, and divided all key frames and their mark files into 14 178 training data and 1 400 verification data, based on YOLOV5s, YOLOV5l, YOLOV5m and TOLOV5x deep learning neural network models. The group training tests were designed to compare training effects. The results showed that the four neural network models trained in this article all completed the target detection task of the tuna longline electronic monitoring system. However, the choice of the network model had a highly significant impact on the parameters of GIoU loss, objectness loss, precision, recall, mAP@0.5, mAP@0.5∶0.95(P<0.05), without significant impact on the classification loss parameters(P<0.05). The better detection results were observed in YOLOV5m network models, with mAP@0.5 values of 99.1% in YOLOV5l network and 99.2% in YOLOV5m network, and the recall rates of 98.4% in YOLOV5l network and 98.3% in YOLOV5m network. However, YOLOV5m was inferior to YOLOV5l in performance such as GIoU loss. The finding indicates that YOLOV5l is the most suitable network model for target detection in tuna longline electronic monitoring system among the four network models of YOLOV5s, YOLOV5l, YOLOV5m and YOLOV5x.
王书献, 张胜茂, 朱文斌, 孙永文, 杨昱皞, 隋江华, 沈烈, 沈介然. 基于深度学习YOLOV5网络模型的金枪鱼延绳钓电子监控系统目标检测应用[J]. 大连海洋大学学报, 2021, 36(5): 842-850.
WANG Shuxian, ZHANG Shengmao, ZHU Wenbin, SUN Yongwen, YANG Yuhao, SUI Jianghua, SHEN Lie, SHEN Jieran. Application of an electronic monitoring system for video target detection in tuna longline fishing based on YOLOV5 deep learning model. Journal of Dalian Ocean University, 2021, 36(5): 842-850.
2021, Vol. 36 
