An Embedding Skeleton for Fish Detection and Marine Organisms Recognition
文献类型：期刊论文
| 作者 | Zhu, Jinde3; He, Wenwu3; Weng, Weidong3; Zhang, Tao4; Mao, Yuze5; Yuan, Xiutang1 ; Ma, Peizhen2; Mao, Guojun3
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| 刊名 | SYMMETRY-BASEL
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 14期号:6页码:18 |
| 关键词 | deep learning computer vision multi-class classification fish detection YOLOv4 |
| DOI | 10.3390/sym14061082 |
| 通讯作者 | Mao, Guojun(19662092@fjut.edu.cn) |
| 英文摘要 | 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. |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000816510800001 |
| 资助机构 | National Key Research and Development Plan of China |
| 源URL | [http://ir.yic.ac.cn/handle/133337/37381]  |
| 专题 | 海岸带生物学与生物资源利用重点实验室 海岸带生物资源高效利用研究与发展中心 |
| 通讯作者 | Mao, Guojun |
| 作者单位 | 1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Dept Marine Organism Taxon & Phylogeny, Qingdao 266071, Peoples R China 3.Fujian Univ Technol, Sch Comp Sci & Math, Fuzhou 350011, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Marine Ecol & Environm Sci, Qingdao 266071, Peoples R China 5.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Jinde,He, Wenwu,Weng, Weidong,et al. An Embedding Skeleton for Fish Detection and Marine Organisms Recognition[J]. SYMMETRY-BASEL,2022,14(6):18. |
| APA | Zhu, Jinde.,He, Wenwu.,Weng, Weidong.,Zhang, Tao.,Mao, Yuze.,...&Mao, Guojun.(2022).An Embedding Skeleton for Fish Detection and Marine Organisms Recognition.SYMMETRY-BASEL,14(6),18. |
| MLA | Zhu, Jinde,et al."An Embedding Skeleton for Fish Detection and Marine Organisms Recognition".SYMMETRY-BASEL 14.6(2022):18. |
入库方式： OAI收割
来源：烟台海岸带研究所
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