Developing machine learning methods for automatic recognition of fishing vessel behaviour in the Scomber japonicus fisheries

文献类型： 外文期刊

第一作者： Wang, Shuxian

作者： Wang, Shuxian;Zhang, Shengmao;Tang, Fenghua;Shi, Yongchuang;Fan, Xiumei;Chen, Junlin;Wang, Shuxian;Zhang, Shengmao;Chen, Junlin;Sui, Yanming

作者机构：

关键词： vessel behaviors recognition; Scomber japonicus; attention mechanism; long short-term memory; deep learning in fisheries

期刊名称：FRONTIERS IN MARINE SCIENCE （ 影响因子：3.7； 五年影响因子：4.7 ）

ISSN：

年卷期： 2023 年 10 卷

页码：

收录情况： SCI

摘要： IntroductionWith a higher degree of automation, fishing vessels have gradually begun adopting a fishing monitoring method that combines human and electronic observers. However, the objective data of electronic monitoring systems (EMS) has not yet been fully applied in various fishing boat scenarios such as ship behavior recognition. MethodsIn order to make full use of EMS data and improve the accuracy of behaviors recognition of fishing vessels, the present study proposes applying popular deep learning technologies such as convolutional neural network, long short-term memory, and attention mechanism to Chub mackerel (Scomber japonicus) fishing vessel behaviors recognition. The operation process of Chub mackerel fishing vessels was divided into nine kinds of behaviors, such as "pulling nets", "putting nets", "fish pick", "reprint", etc. According to the characteristics of their fishing work, four networks with different convolutional layers were designed in the pre-experiment. And the feasibility of each network in behavior recognition of the fishing vessels was observed. The pre-experiment is optimized from the perspective of the data set and the network. From the standpoint of the data set, the size of the optimized data set is significantly reduced, and the original data characteristics are preserved as much as possible. From the perspective of the network, different combinations of pooling, long short-term memory(LSTM) network, and attention(including CBAM and SE) are added to the network, and their effects on training time and recognition effect are compared. ResultsThe experimental results reveal that the deep learning methods have outstanding performance in behaviors recognition of fishing vessels. The LSTM and SE module combination produced the most apparent optimization effect on the network, and the optimized model can achieve an F1 score of 97.12% in the test set, surpassing the classic ResNet, VGGNet, and AlexNet. DiscussionThis research is of great significance to the management of intelligent fishery vessels and can promote the development of electronic monitoring systems for ships.

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