一个改进的YOLOv8n用于在自然水环境中检测鱼类
Zehao Zhang1,2,3,4, Yi Qu1,2,3,4, Tan Wang1,2,3,4
1School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.
Animals : an open access journal from MDPI
|July 27, 2024
概括
本研究介绍了BSSFISH-YOLOv8,这是一种准确的计算机视觉方法,用于在水下环境中检测鱼类. 它提高了检测效率,并通过提高小目标的准确性来降低渔业调查的成本.
科学领域:
- 渔业 科学 渔业 科学
- 计算机视觉 计算机视觉
- 海洋生物学 海洋生物学
背景情况:
- 由于环境的复杂性,水下摄影对准确的鱼类检测提出了挑战.
- 现有的计算机视觉方法对渔业资源调查的准确性有局限性.
- 提高检测效率和降低成本对于有效的渔业管理至关重要.
研究的目的:
- 开发一种准确和高效的计算机视觉方法,用于在自然水下环境中检测鱼类.
- 解决当前处理水下图像复杂性和小目标的方法的局限性.
- 加强计算机视觉在渔业资源调查中的应用.
主要方法:
- 拟议的 BSSFISH-YOLOv8 模型包含 SPD-Conv 模块,以保存细粒度的信息.
- 集成BiFormer,一个动态的稀疏注意力技术,进入骨干网络,以提高功能重点和效率.
- 引入了160×160小目标检测层 (STDL),以提高对较小鱼的敏感性.
主要成果:
- BSSFISH-YOLOv8获得了88.3%的mAP@50和58.3%的mAP@50:95.
- 性能指标分别比YOLOv8n模型高2.0%和3.3%.
- 在复杂的水下条件下检测鱼类的准确性和效率有所提高.
结论:
- BSSFISH-YOLOv8为水下环境中的鱼类检测提供了重大进展.
- 该方法有可能降低测量成本并提高渔业资源调查的效率.
- 这项研究通过加强技术应用,为可持续的渔业管理做出了贡献.
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