在室内住房条件下用深度学习模型检测牛关键点
M Taghavi1, H Russello2, W Ouweltjes1
1Wageningen Livestock Research, Wageningen University and Research, 6708 WD Wageningen, the Netherlands.
Journal of dairy science
|October 20, 2023
概括
使用T-LEAP模型自动化乳牛关键点检测可以改善的监测. 这项技术可以为更好的动物福利和可持续的乳制品生产提供早期干预.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 计算机视觉 计算机视觉
背景情况:
- 乳牛的是重要的经济和动物福利问题.
- 目前的的检测方法是劳动密集型和不频繁的.
- 自动步态评估为频繁,低劳动力移动监测提供了潜力.
研究的目的:
- 在室内农场环境中自动检测乳牛的关键解剖点.
- 开发关键点的后处理步骤,使步行特征计算成为可能.
- 在真实的农场环境中为基于摄像头的自动移动监控奠定基础.
主要方法:
- 使用了34只荷尔斯坦-弗里西亚奶牛的侧面视频录像.
- 训练了一种深度学习模型 (T-LEAP) 来检测牛的17个关键点.
- 通过使用正确关键点的百分比 (PCKh0.2) 评估模型性能,在测试组中总体达到89%.
主要成果:
- 在T-LEAP模型中,在乳牛的17个关键点上检测了89%的PCKh0.2.
- 牛背上的关键点显示了最差的检测性能 (59% PCKh0.2),特别是在水平方向.
- 确定了必要的后处理步骤,包括摄像头扭曲校正和错误的关键点纠正.
结论:
- 自动关键点检测在实际的室内农场环境中是可行的,即使有遮蔽.
- 后处理技术对于将检测到的关键点转化为可靠的步行特征至关重要.
- 这项研究为开发自动化机动监控系统提供了必要的先决条件,以改善乳牛福利.
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