Journal of Dalian Ocean University  2022, Vol. 37 Issue (4): 683-695 DOI: 10.16535/j.cnki.dlhyxb.2022-099 |
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| Research progress in the application of deep learning methods for marine fishery production: a review |
| ZHANG Shengmao, SUN Yongwen, FAN Wei, TANG Fenghua, CUI Xuesen, WU Yumei
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| 1.College of Navigation and Ship Engineering, Dalian Ocean University, Dalian 116023, China; 2.Key Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China |
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Abstract With the continuous decline of global fishery resources, fishery agencies and regional fisheries management organizations of various countries use the fishery observer method to promote sustainable fishing, but the human observer method is characterized by so high cost and low coverage as to difficult to meet the management needs. In recent years, detection speed and accuracy have been continuously enhanced due to continuous emergence and improvement of new deep learning algorithms, thus providing conditions for marine fishery fishing production monitoring. The process of building a fishery production monitoring model is introduced from the aspects of data acquisition, data preprocessing, algorithm design, model training, and model accuracy evaluation. The application of deep learning technology in marine fishery fishing is discussed, and methods such as transfer learning or reinforcement learning are proposed in terms of fishing boats and boat behavior, catches, fishery forecasts, crew members, and fishing gear to expand the identification of target species and enhance detection algorithm，and use high-accuracy feature extraction network to improve the accuracy of target classification, to solve the real-time analysis of electronic monitoring data through edge computing technology and to formulate standards and specifications for electronic monitoring fishery management applications and other key research directions in the future, which provides reference for the promotion of deep learning in marine fishery fishing production.
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Published: 19 September 2022
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