YOLOv5-KCB:一种使用优化的K-Means,CA注意力机制和双向特征金字塔网络的个人猪检测的新方法
Guangbo Li1, Guolong Shi1,2,3, Jun Jiao1
1School of Information and Computer, Anhui Agricultural University, Hefei 230036, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究介绍了YOLOv5-KCB,这是一种用于非侵入性个人猪识别的先进算法. 它通过使用头部和部数据显著提高了识别猪的准确性,增强了智能养殖实践.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 传统的猪耳标签是劳动密集型和不准确的.
- 智能养猪需要可靠的个体识别方法.
研究的目的:
- 为准确的个体猪识别开发一种非侵入性算法.
- 改进现有的猪识别技术,以实现智能养殖.
主要方法:
- 提出了使用猪脸部和部数据集 (19,680个样本) 的YOLOv5-KCB算法.
- 修改的K-平均集群使用1IOU距离度量.
- 集成的CA注意力机制和BiFPN用于特征融合.
主要成果:
- 在个人猪识别方面,YOLOv5-KCB实现了卓越的准确性.
- 头部和部识别准确度达到98.4%;面部识别是95.1%.
- 与原来的YOLOv5算法相比,显示出了显著的改进.
结论:
- YOLOv5-KCB为个人猪识别提供了精确有效的解决方案.
- 该算法支持智能猪管理系统的发展.
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