Accurate localization of keypoints for animal body measurement is of great importance for intelligent livestock health monitoring and automated measurement systems. However, existing keypoint detection models often suffer from two major limitations: a large number of parameters which are unfavorable for deployment on edge devices, and insufficient localization accuracy in some lightweight models that fails to meet the precision requirements of body measurement tasks. To address these challenges, this paper proposes a lightweight keypoint detection model based on an improved YOLOv8-Pose architecture, which effectively enhances detection accuracy while maintaining real-time performance. In the proposed model, coordinate attention mechanisms are integrated into the shallow layers of the backbone network and before the three heads, enabling spatial coordinate information to be effectively embedded into channel attention. This design enhances the model ability to perceive the spatial distribution of keypoints, thereby improving the accuracy and stability of keypoint localization. In addition, a forced sharpening module is introduced in the shallow backbone to strengthen edge and contour features, facilitating a better balance between global semantic representation and local detail preservation. Furthermore, a high-resolution feature branch is incorporated to provide fine-grained spatial information to the three detection heads, compensating for the loss of spatial resolution in deeper feature maps. Experimental results demonstrate that the proposed model achieves a precision of 82.6%, a recall of 81.3%, and an mAP50-95 of 58.1% on complex horse keypoint detection tasks. Compared with YOLOv8-Pose, the proposed method achieves improvements of 21.0%, 39.2%, and 45.3% in precision, recall, and mAP50-95, respectively, exhibiting superior robustness and localization capability under complex poses and diverse environmental conditions. In terms of model complexity, the number of parameters increases from 3.361 M to 4.611 M, corresponding to an increase of approximately 37.2%, while the floating-point operations rises from 9.6×10⁹ to 10.4×10⁹, an increase of only 8.3%. Despite this limited computational overhead, the proposed model delivers substantial performance gains without significantly affecting inference speed. These results indicate that the proposed approach achieves a favorable trade-off between accuracy and efficiency by incurring a relatively small model cost for considerable performance improvement, demonstrating its strong potential for intelligent livestock farming and automated body measurement applications.