文献类型： 外文期刊

作者： Wang, Jiaming ¹ ; Yin, Xiangbo ² ; Li, Guodong ² ;

作者机构： 1.Shanghai Ocean Univ, Coll Informat Technol, Shanghai, Peoples R China

2.Chinese Acad Fishery Sci, Fishery Machinery & Instrument Res Inst, Shanghai 200092, Peoples R China

关键词： YOLOv5s; deck crew detection; fishing net detection; deep learning model lightweight

期刊名称：FISHES （ 2022影响因子：2.3； 五年影响因子：2.4 ）

ISSN：

年卷期： 2023 年 8 卷 7 期

页码：

收录情况： SCI

摘要： A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of a fishing net detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces the backbone of YOLOv5s with ShuffleNetV2, replaces the feature fusion network with a modified Generalized-FPN, and adds the CBAM attention module in front of the detection head.