编程和设置物体检测算法YOLO以确定肉牛的养活动:YOLOv8m和YOLOv10m之间的比较
Pablo Guarnido-Lopez1, John-Fredy Ramirez-Agudelo2, Emmanuel Denimal1
1Institut Agro Dijon, 26 bd Docteur Petitjean, 21079 Dijon, France.
Animals : an open access journal from MDPI
|October 16, 2024
概括
使用YOLO物体检测监测牛的养行为显示,YOLOv10略高于YOLOv8. 这两种算法都实现了高精度,但YOLOv10为实际农场应用提供了更好的性能.
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:
- 精确监测牛的养行为对于牲畜管理和福利至关重要.
- 对象检测算法为行为分析提供了一个有前途的非侵入性方法.
- 之前的研究已经探索了各种计算机视觉技术用于动物行为监测.
研究的目的:
- 为了比较YOLOv8和YOLOv10对象检测算法的性能,用于识别牛的养行为.
- 在现实世界农场环境中评估这些算法的有效性.
- 确定最适合用于实际牛行为监测的YOLO版本.
主要方法:
- 一个法国农场录制了六头夏洛莱公牛的视频.
- 使用Roboflow识别和标记了三种养行为 (咬,,拜访).
- 使用YOLOv8和YOLOv10评估了对象检测性能,比较了精度,回忆和mAP分数.
主要成果:
- 与YOLOv8.8相比,YOLOv10的精度,回忆,mAP50和mAP50-95得分略高一些.
- 这两种算法的整体准确率约为90%.
- YOLOv8训练得更快,并显示出较少的过拟合,而YOLOv10在预测中表现出更好的一致性.
结论:
- 无论YOLOv8和YOLOv10都有效地检测牛的养行为.
- YOLOv10显示出优越的平均性能,学习率和速度,使其更适合现场应用.
- 进一步的研究可以探索实时实现和更大的数据集,以提高准确性.
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