YOLOv8-CBAM:在Ujumqin羊群中进行一项关于羊头识别的研究
Qing Qin1,2,3, Xingyu Zhou1,2,3, Jiale Gao1,2,3
1Animal Science Department, Inner Mongolia Agricultural University, Hohhot City, China.
Frontiers in veterinary science
|February 21, 2025
概括
一个新的YOLOv8-CBAM模型准确地识别了Ujumqin羊的面部颜色. 这种先进的头部颜色识别系统改善了品种识别,并支持自动化绵羊管理.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 羊的面部颜色对于品种识别,遗传多样性评估和选择性繁殖至关重要.
- 准确的识别方法对于有效的畜牧管理和研究至关重要.
研究的目的:
- 开发和评估Ujumqin羊的新型头部颜色识别模型.
- 通过深度学习提高羊面部颜色识别的准确性和效率.
主要方法:
- 收集了来自201只乌玉金羊的1713张头像.
- 使用YOLOv8架构和CBAM注意力机制开发了一个头部颜色识别模型 (YOLOv8-CBAM).
- 特定于Ujumqin羊面部颜色的特征点.
主要成果:
- 对于识别四个头部颜色类别,YOLOv8-CBAM模型实现了97.7%的平均精度 (mAP) 和0.94的F1得分.
- 在平均精度方面,它表现优于各种YOLOv8变体,YOLOv3,YOLOv5n和YOLOv10n.
- 证明了卓越的性能,特别是在较小的模型配置中.
结论:
- YOLOv8-CBAM模型为Ujumqin羊提供高度准确的面部颜色识别.
- 这项技术为开发自动化绵羊管理系统提供了可靠的技术支持.
- 这些发现突显了注意力机制在改善物体检测以识别牲畜方面的有效性.
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