GRMPose: GCN-based real-time dairy goat pose estimation Chen Chao Rui An Ruizi Han

Pose estimation can be used to analyse the behaviour of livestock, hence offering valuable insights into the physiological state of the livestock. Despite impressive progress in academic benchmarks, existing pose estimation methods do not meet the speed and accuracy trade -off requirements of industrial applications. Following the top-down paradigm, we propose GRMPose, a GCN-based real-time pose estimation framework for dairy goats. The framework adopts CSPNext as its backbone, which is highly efficient and potent for extracting features. In addition, a GCN-based coordination classification module (GCNCC) is proposed to improve the capability of extracting the structural pose information of the keypoint features. To assess the effectiveness of GRMPose, we built the DairyGoat dataset, which contains 2,108 images and 2,576 instances with different lighting conditions, different types of behaviours, and partially occluded dairy goat instance objects. Experimental results demonstrate that GRMPose achieves an AP of 87.48% on the DairyGoat dataset. Additionally, it exhibits a model inference latency of 13.82 ms, GFLOPs of 5.57, and parameters totalling 27.56 million. These results establish GRMPose as superior to other models like HRNet, Resnext, and MobileNetv2 in terms of speed-accuracy trade -off.