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基于关键点和单眼深度估计的姿势估计,用于预测牛的体重和部高度

Guilherme L Menezes¹, Alyssa Seitz¹, Enrico Casella²

¹Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, 53703, United States.

Journal of animal science

|February 18, 2026

概括

通过使用具有姿势估计和单眼深度估计 (MDE) 的二维图像,可以预测牛的体重和部高度. 这种计算机视觉方法为畜牧监测3D系统提供了一种经济有效的替代方案.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
动物科学动物科学

背景情况:

传统的计算机视觉系统 (CVS) 用于预测牛的体重 (BW) 和部高度 (HH),通常依赖于昂贵的3D成像或不切实际的侧视2D摄像头.
顶向下视图2D成像与姿势估计相结合,为提取生物识别特征提供了潜在的解决方案.
单眼深度估计 (MDE) 可以从二维图像中生成深度信息,进一步增强生物识别分析.

研究的目的:

开发BW和HH的预测模型,使用身体姿势关键点的特征和MDE生成的深度图像从上下2D红外图像.
将这些模型的预测性能与使用3D成像系统特征的模型进行比较.

主要方法:

采集了395个由上向下视图视频,使用红外和深度传感器对肉牛和奶牛杂交.
利用姿势估计模型确定了七个关键的解剖学地标.
应用零射击MDE将2D红外图像转换为3D表示和提取的特征 (体积,面积等). ) 的情况.
从使用相同特征提取管道的3D成像系统处理深度图像.
评估随机森林,部分最小平方回归 (PLS) 和支持矢量回归模型,使用离开一个块的交叉验证.

主要成果:

使用关键点衍生特征的PLS模型实现了BW的R2为0.90,HH的R2为0.77.
使用MDE衍生深度特征的PLS模型表现出卓越的性能,BW (RMSE 24.2公斤) 的R2为0.95,HH的R2为0.95,HH的结果相似.
使用2D衍生功能的预测性能与3D成像系统的性能相当.

结论:

从上下2D图像中提取的生物特征,包括通过MDE生成的生物特征,为牛的BW和HH提供了有效和可比的预测.
这种方法为传统的3D成像系统提供了一种具有成本效益和实用的替代方案,用于牲畜监测和管理.

关键词:

动物生长动物生长牛肉料区提供牛肉料.在乳制品上的牛肉.深度任何东西任何东西构成估计构成估计

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