Shen Yuhao, Li Baoshan, Wang Yueming, Li Qi, Zhang Zhirong
Inner Mongolia University of Science and Technology, School of Digital and Intelligence Industry, Baotou, 014010, China; Grassland Animal Husbandry Artificial Intelligence Inner Mongolia Autonomous Region Engineering Research Center, Baotou, 014010, China.
Grassland Animal Husbandry Artificial Intelligence Inner Mongolia Autonomous Region Engineering Research Center, Baotou, 014010, China.
J Dairy Sci. 2025 Aug 6. doi: 10.3168/jds.2025-26299.
Lameness has significant effects on the health, welfare, and productivity of dairy cows. Common challenges in farm environments, such as uneven lighting and occlusion, reduce the accuracy of keypoint detection, which in turn affects the precise extraction of motion features. Moreover, a single motion feature is often insufficient to comprehensively reflect lameness behavior. This study explores a lameness detection method for dairy cows based on the integration of keypoint-derived motion features. First, to enhance the accuracy of cow keypoint detection, improvements were made to YOLOv8-Pose-the keypoint detection module in the YOLOv8 framework-to boost performance under complex environmental conditions, and its positive effect on lameness classification was validated. Next, the improved model was used to detect keypoints on the hooves, knees, hips, and head-neck region of the cows. From these, 3 types of temporal motion features were extracted: relative displacement between fore and hind hooves, hoof movement speed, and head-neck motion trajectory. Each feature type was individually used for lameness classification using a Conv2D-LSTM structure, which combines convolutional operations with a long short-term memory (LSTM) network for temporal modeling. Finally, to achieve more robust lameness detection results, the stacking method from ensemble learning was applied to fuse the predictions based on the 3 types of features. Results show that the improved YOLOv8-Pose model can effectively detect cow keypoints, achieving a precision of 99.4%, recall of 96.9%, mAP@0.5 of 97.8%, mAP@0.75 of 88.0%, and mAP@0.5:0.95 of 79.3% (where mAP refers to mean average precision, a standard metric for detection accuracy). Among 141 cow samples with a lameness prevalence of 52.5%, the average classification accuracy for each of the 3 motion features exceeded 85%, whereas the integrated method based on keypoint motion features achieved an overall accuracy of 97.2%. Cross-validation further confirms the accuracy and generalization capability of the proposed algorithm, offering a feasible path for intelligent lameness monitoring in dairy cows.
跛行对奶牛的健康、福利和生产力有重大影响。农场环境中的常见挑战，如光照不均匀和遮挡，会降低关键点检测的准确性，进而影响运动特征的精确提取。此外，单一的运动特征往往不足以全面反映跛行行为。本研究探索了一种基于关键点衍生运动特征整合的奶牛跛行检测方法。首先，为提高奶牛关键点检测的准确性，对YOLOv8-Pose（YOLOv8框架中的关键点检测模块）进行了改进，以提升其在复杂环境条件下的性能，并验证了其对跛行分类的积极影响。接下来，使用改进后的模型检测奶牛蹄部、膝盖、髋部和头颈区域的关键点。从中提取了3种时间运动特征：前后蹄之间的相对位移、蹄部运动速度和头颈运动轨迹。每种特征类型都使用Conv2D-LSTM结构单独用于跛行分类，该结构将卷积操作与长短期记忆（LSTM）网络相结合进行时间建模。最后，为获得更稳健的跛行检测结果，应用集成学习中的堆叠方法融合基于这3种特征类型的预测结果。结果表明，改进后的YOLOv8-Pose模型能够有效检测奶牛关键点，精度达到99.4%，召回率为96.9%，mAP@0.5为97.8%，mAP@0.75为88.0%，mAP@0.5:0.95为79.3%（其中mAP指平均精度均值，是检测准确性的标准指标）。在141个跛行患病率为52.5%的奶牛样本中，3种运动特征各自的平均分类准确率均超过85%，而基于关键点运动特征的集成方法总体准确率达到97.2%。交叉验证进一步证实了所提算法的准确性和泛化能力，为奶牛跛行的智能监测提供了一条可行途径。
