Recognition of Acetes chinensis fishing vessel based on 3-2D integration model behavior
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摘要: 针对中国毛虾 (Acetes chinensis) 产量逐年锐减问题，中国开始对近海海域实施毛虾限额捕捞措施，采用视频监控技术辅助捕捞管理。提出一种基于3-2D融和模型的毛虾捕捞渔船行为识别方法，为限额捕捞管理提供新的解决方案。通过在毛虾渔船上4个固定位置安装高清摄像设备，并记录捕捞作业全过程，共获取600余个视频监控数据作为初始数据；从初始数据中筛选有效的视频数据，同时对视频数据进行5种行为的划分和标记。为了提高网络训练的效率，对视频数据进行压缩和帧数分割等预处理；最后，通过搭建3-2D融合的卷积神经网络来训练模型，实现渔船行为特征的提取和分类。结果表明，捕捞渔船行为识别方法的分类精度为95.35%，召回率为94.50%，平均精确度为96.60%，模型整体得分达93.32%，平均检测时间为35.46 ms·帧−1，可用于毛虾渔船捕捞视频的实时分析。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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Key words:
- Acetes chinensis /
- Quota fishing /
- Deep learning /
- Convolutional neural network
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图 4 捕捞渔船初始拍摄数据统计图
Figure 4. Statistics chart of initial shooting data of fishing vessel
图 6 3-2D融合模型的卷积神经网络结构图
Figure 6. Convolutional neural network structure diagram of 3-2D fusion model
表 1 选用视频数据
Table 1. Selection of video data
渔船标签
Fishing vessel label渔船行为
Fishing vessel behavior渔船视频数量
Number of fishing vessel videos/个0 停靠 Dock 90 1 航行 Sail 110 2 下网 Cast net 110 3 收网 Put away net 110 4 等待 Wait 80 
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表 2 制作数据标签
Table 2. Production of data labels
视频路径
Video path视频帧数
Video frames捕捞渔船标签
Fishing vessel label行为状态
Behavioral statesyy/d01/D01_20210617171455.mp4 0~13 230 0 停靠 Dock syy/d01/D01_20210620043651.mp4 0~11 940 1 航行 Sail syy/d01/D01_20210620043651.mp4 11 940~50 093 2 下网 Cast net syy/d01/D01_20210630181131.mp4 0~69 301 3 收网 Put away net syy/d01/D01_20210620061938.mp4 0~4 330 4 等待 Wait
下载: 导出CSV
表 3 模型评价主要指标及结果
Table 3. Main indicators and results of model evaluation
指标
Index训练结果
Training result测试结果
Test result精度 Precision/% 99.60 95.35 召回率 Recall rate/% 99.63 94.50 平衡F分数 F1-Score/% 99.06 93.32 平均精确度 AP/% 98.70 96.60 时间 t/(ms·帧−1) — 35.46
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