A dynamic yak heifer pose estimation model based on keypoints detection for complex environmental monitoring.

Behavior is an important indicator of yak heifer welfare and health status, with key behavioral patterns reflecting critical conditions, including fattening, reproduction, and disease. Computer vision-based pose estimation has become an essential technology for livestock behavior monitoring. This study developed a yak heifer pose estimation model named Multistage Feature Attention PVT-based Dynamic Yak Heifer Pose Estimation Model (MFPVT-YakHeifer) based on improved Pyramid Vision Transformer version 2 (PVT v2) and keypoint detection modeling. The proposed and compared models were trained with a novel YakPoseData set encompassing yak heifer images collected in different poses and environmental conditions. The results showed that the model achieved performance metrics of 90.41% mean average precision at an intersection over union (IoU) threshold of 0.5, 64.37% mean average precision at 0.75 IoU, 64.11% mean average precision, and 91.95% mean average recall at 0.5 IoU, all of which are higher than those of the other 4 benchmark models. Finally, the MFPVT-YakHeifer model has been deployed on edge computing device for livestock farm applications. The future work will focus on animal dataset expansion, real-time video analysis implementation, and computational efficiency optimization for edge deployment.