UST-YOLO11Pose-TRM: An Attention-Enhanced Keypoint Detection and Transformer Regression Framework for Yak Body...

Yak (Bos grunniens) is a vital livestock resource on the Qinghai-Tibet Plateau, and its body measurement parameters play a crucial role in growth and development assessment, health monitoring, and breeding improvement. To overcome the limitations of traditional manual measurements-such as low efficiency, unstable accuracy, and the tendency to induce animal stress-this study proposes an intelligent yak body measurement prediction method that integrates keypoint detection with regression modeling, termed UST-YOLO11Pose-TRM. Within the YOLO11-Pose framework, three attention mechanisms-UIB, SENetV2, and TripleAttention-are incorporated to construct a lightweight yet high-precision keypoint detection model, UST-YOLO11Pose, thereby enhancing channel feature representation, global contextual modeling, and spatial dependency perception. Meanwhile, a Transformer-based regression model is designed, leveraging multi-head self-attention to characterize global geometric relationships among keypoints and to achieve accurate prediction of key body measurement parameters, including body length, body height, oblique body length, chest girth, and cannon circumference. Experimental results demonstrate that UST-YOLO11Pose achieves an mAP of 0.958, a Precision of 0.967, and a Recall of 0.955 in keypoint detection tasks, significantly outperforming both same-series and cross-series comparative models with a parameter size of only 10.06 MB. In the body measurement regression task, the Transformer-based regression model attains an RMSE of 0.185, an MAE of 0.122, an MAPE of 2.3%, and a coefficient of determination (R²) of 0.962 on the test set, indicating excellent predictive accuracy and robust fitting stability. In summary, UST-YOLO11Pose-TRM enables accurate, efficient, non-contact yak body measurement, showing strong potential for smart pasture development and precision livestock management.