Recognition on the working status of Acetes chinensis quota fishing vessels based on a 3D convolutional neural network
文献类型： 外文期刊
作者： Wang, Shuxian 1 ; Zhang, Shengmao 1 ; Liu, Yang 2 ; Zhang, Jiaze 2 ; Sun, Yongwen 1 ; Yang, Yuhao 1 ; Hu, Huijuan 2 ; Xiong, Ying 3 ; Fan, Wei 2 ; Wang, Fei 2 ; Tang, Fenghua 2 ;
作者机构： 1.Dalian Ocean Univ, Sch Nav & Naval Architecture, Dalian 116023, Peoples R China
2.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China
3.Jiangsu Marine Fisheries Res Inst, Nantong 226007, Peoples R China
4.Shanghai Ocean Univ, Coll Informat Technol, Shanghai 200090, Peoples R China
关键词： Acetes chinensis; Quota fishing; Fishing vessels status recognition; 3D convolutional neural network
期刊名称：FISHERIES RESEARCH （ 影响因子：2.815； 五年影响因子：2.884 ）
ISSN： 0165-7836
年卷期： 2022 年 248 卷
页码：
收录情况： SCI
摘要： In the present study, a method for identifying the status of Acetes chinensis fishing vessels based on a 3D convolutional neural network is proposed, so as to protect marine biodiversity, monitor the working status of Acetes chinensis fishing vessels and assist in the realization of quota fishing. The Vessel Monitoring System (VMS) was installed on the quota fishing vessels to collect work data from June 16, 2021 to July 13, 2021. According to the characteristics of the fishing vessels, the work status of the fishing vessels was divided into five statuses such as stopping, sailing, putting net, waiting and pulling net. The 3D convolutional neural network Acetes3DNet was designed to extract the multi-dimensional and multi-level features of the data and trained in the training set. Finally, the effectiveness of the model was verified in the validation set. The training results were combined with the Beidou ship position data to restore the working process of the fishing vessel. The experimental results reveal that after 150 epochs of training, the precision, recall, and f1 score of Acetes3DNet on the training set reached 99.02%, 99.19%, and 99.09%, respectively, while the precision, recall, and f1 score on the validation set reached 97.09%, 96.82%, and 96.68%. Research shows that Acetes3DNet can circumvent the limitations of traditional 2D neural networks in dynamic target detection, complete recognition of the working status of Acetes chinensis quota fishing vessels, and show the historical work process of the ship in an intuitive manner. The experimental results are conducive to standardizing the management of fishing vessels and protecting marine life.
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