文献类型： 外文期刊
作者： Song, Yifan 1 ; Zhang, Shengmao 1 ; Tang, Fenghua 1 ; Shi, Yongchuang 1 ; Wu, Yumei 1 ; He, Jianwen 3 ; Chen, Yunyun 4 ; Li, Lin 5 ;
作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab Fisheries Remote Sensing, Minist Agr & Rural Affairs, Shanghai 200090, Peoples R China
2.Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China
3.China Agr Dev Grp Zhoushan Ocean Fishing Co Ltd, Zhoushan 316100, Peoples R China
4.China Aquat Prod Zhoushan Marine Fisheries Corp Co, Zhoushan 316100, Peoples R China
5.Inspur Grp Co Ltd, Jinan 250000, Peoples R China
关键词： deep learning; jigger behavior identification; squid fishing vessel
期刊名称：FISHES （ 影响因子：2.3； 五年影响因子：2.4 ）
ISSN：
年卷期： 2023 年 8 卷 10 期
页码：
收录情况： SCI
摘要： In recent years, with the development of pelagic fishing, the working environment and monitoring of crew (squid jigger) members have become increasingly important. However, traditional methods of pelagic human observers suffer from high costs, low coverage, poor timeliness, and susceptibility to subjective factors. In contrast, the Electronic Monitoring System (EMS) has advantages such as continuous operation under various weather conditions; more objective, transparent, and efficient data; and less interference with fishing operations. This paper shows how the 3DCNN model, LSTM+ResNet model, and TimeSformer model are applied to video-classification tasks, and for the first time, they are applied to an EMS. In addition, this paper tests and compares the application effects of the three models on video classification, and discusses the advantages and challenges of using them for video recognition. Through experiments, we obtained the accuracy and relevant indicators of video recognition using different models. The research results show that when NUM_FRAMES is set to 8, the LSTM+ResNet-50 model has the best performance, with an accuracy of 88.47%, an F1 score of 0.8881, and an map score of 0.8133. Analyzing the EMS for pelagic fishing can improve China's performance level and management efficiency in pelagic fishing, and promote the development of the fishery knowledge service system and smart fishery engineering.
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