Zhang Ruihong, Zhao Kaixuan, Ji Jiangtao, Wang Jinjin
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Animals (Basel). 2024 Dec 26;15(1):30. doi: 10.3390/ani15010030.
Top-view systems for lameness detection have advantages such as easy installation and minimal impact on farm work. However, the unclear lameness motion characteristics of the back result in lower recognition accuracy for these systems. Therefore, we analysed the compensatory behaviour of cows based on top-view walking videos, extracted compensatory motion features (CMFs), and constructed a model for recognising lameness in cows. By locating the hook, pin, sacrum, and spine positions, the motion trajectories of key points on the back were plotted. Based on motion trajectory analysis of 655 samples (258 sound, 267 mild lameness, and 130 severe lameness), the stability mechanisms of back movement posture were investigated, compensatory behaviours in lame cows were revealed, and methods for extracting CMFs were established, including swing and posture features. The feature correlation among differently scoring samples indicated that early-stage lame cows primarily exhibited compensatory swing, while those with severe lameness showed both compensatory swing and posture. Lameness classification models were constructed using machine learning and threshold discrimination methods, achieving classification accuracies of 81.6% and 83.05%, respectively. The threshold method reached a recall rate of 93.02% for sound cows. The proposed CMFs from back depth images are highly correlated with early lameness, improving the accuracy of top-view lameness detection systems.
用于跛行检测的俯视系统具有安装简便且对农场作业影响最小等优点。然而，牛背部跛行运动特征不清晰导致这些系统的识别准确率较低。因此，我们基于俯视行走视频分析了奶牛的代偿行为，提取了代偿运动特征（CMFs），并构建了奶牛跛行识别模型。通过定位牛钩、荐结节、骶骨和脊柱的位置，绘制了牛背部关键点的运动轨迹。基于对655个样本（258个健康、267个轻度跛行和130个重度跛行）的运动轨迹分析，研究了牛背部运动姿势的稳定机制，揭示了跛行奶牛的代偿行为，并建立了包括摆动和姿势特征在内的CMFs提取方法。不同评分样本之间的特征相关性表明，早期跛行奶牛主要表现出代偿性摆动，而重度跛行奶牛则同时表现出代偿性摆动和姿势变化。使用机器学习和阈值判别方法构建了跛行分类模型，分类准确率分别达到81.6%和83.05%。阈值方法对健康奶牛的召回率达到93.02%。从牛背部深度图像中提取的CMFs与早期跛行高度相关，提高了俯视跛行检测系统的准确性。
