Volume 18 Issue 4
Aug.  2022
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ZHANG Jiaze, ZHANG Shengmao, WANG Shuxian, YANG Yuhao, DAI Yang, XIONG Ying. Recognition of Acetes chinensis fishing vessel based on 3-2D integration model behavior[J]. South China Fisheries Science, 2022, 18(4): 126-135. DOI: 10.12131/20210263
Citation: ZHANG Jiaze, ZHANG Shengmao, WANG Shuxian, YANG Yuhao, DAI Yang, XIONG Ying. Recognition of Acetes chinensis fishing vessel based on 3-2D integration model behavior[J]. South China Fisheries Science, 2022, 18(4): 126-135. DOI: 10.12131/20210263
shu

Recognition of Acetes chinensis fishing vessel based on 3-2D integration model behavior

  • 1.

    College of Information, Shanghai Ocean University, Shanghai 201306, China

  • 2.

    East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences/Key Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, Shanghai 200090, China

  • 3.

    School of Navigation and Naval Architecture, Dalian Ocean University, Dalian 116023, China

  • 4.

    Jiangsu Marine Fisheries Research Institute, Nantong 226007, China

More Information
  • Received Date: September 11, 2021
  • Revised Date: November 01, 2021
  • Accepted Date: November 16, 2021
  • Available Online: December 06, 2021
    Graphical Abstract
    • Abstract
  • Since the yield of Acetes chinensis has decreased sharply year by year, China has begun to implement quota fishing measures for A. chinensis in offshore waters by using video surveillance technology to assist the fishing management. This paper proposes a method for identifying the behavior of A. chinensis fishing vessels based on the 3-2D fusion model, so as to provide a new solution for quota fishing management. By installing high-definition camera equipment at four fixed positions on the A. chinensis fishing vessel and recording the entire process of fishing operations, we had obtained more than 600 video surveillance data had been as initial data. Secondly, we filtered effective video data from the initial data, and divided and labeled them with five behaviors. In order to improve the efficiency of network training, we preprocessed the video data such as compression and frame number segmentation. Finally, the model was trained by building a 3-2D fusion convolutional neural network to realize the extraction and classification of fishing vessel behavior characteristics. The results show that the classification accuracy of the fishing vessel behavior recognition method was 95.35%; the recall rate was 94.50%; the average accuracy was 96.60%; the overall score of the model could reach 93.32%; and the average detection time was 35.46 ms·frame−1. The method can be used for real-time analysis of the fishing video of A. chinensis fishing boats.
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