An Embedding Skeleton for Fish Detection and Marine Organisms Recognition

文献类型： 外文期刊

第一作者： Zhu, Jinde

作者： Zhu, Jinde;He, Wenwu;Weng, Weidong;Mao, Guojun;Zhang, Tao;Mao, Yuze;Yuan, Xiutang;Ma, Peizhen

作者机构：

关键词： deep learning; computer vision; multi-class classification; fish detection; YOLOv4

期刊名称：SYMMETRY-BASEL （ 影响因子：2.94； 五年影响因子：2.834 ）

ISSN：

年卷期： 2022 年 14 卷 6 期

页码：

收录情况： SCI

摘要： The marine economy has become a new growth point of the national economy, and many countries have started to implement the marine ranch project and made the project a new strategic industry to support vigorously. In fact, with the continuous improvement of people's living standards, the market demand for precious seafood such as fish, sea cucumbers, and sea urchins increases. Shallow sea aquaculture has extensively promoted the vigorous development of marine fisheries. However, traditional diving monitoring and fishing are not only time consuming but also labor intensive; moreover, the personal injury is significant and the risk factor is high. In recent years, underwater robots' development has matured and has been applied in other technologies. Marine aquaculture energy and chemical construction is a new opportunity for growth. The detection of marine organisms is an essential part of the intelligent strategy in marine ranch, which requires an underwater robot to detect the marine organism quickly and accurately in the complex ocean environment. This paper proposes a method called YOLOv4-embedding, based on one-stage deep learning arithmetic to detect marine organisms, construct a real-time target detection system for marine organisms, extract the in-depth features, and improve the backbone's architecture and the neck connection. Compared with other object detection arithmetics, the YOLOv4-embedding object detection arithmetic was better at detection accuracy-with higher detection confidence and higher detection ratio than other one-stage object detection arithmetics, such as EfficientDet-D3. The results show that the suggested method could quickly detect different varieties in marine organisms. Furthermore, compared to the original YOLOv4, the mAP75 of the proposed YOLOv4-embedding improves 2.92% for the marine organism dataset at a real-time speed of 51 FPS on an RTX 3090.

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