Accurate Pose Detection Method for Rail Fastener Clips Based on Improved YOLOv8-Pose

Minor displacements and deflections of rail fastener clips pose a critical risk to railway safety, which are difficult to quantify accurately using traditional object detection methods. This paper proposes an improved YOLOv8-pose-based method, You Only Look Once version 8-pose with GAM, SPPF-Attention, and Wise-IoU (YOLOv8-pose-GSW) for automated and quantitative pose detection of fastener clips. Firstly, a high-precision keypoint detection network is constructed by integrating a Global Attention Mechanism (GAM) into the neck, enhancing the Spatial Pyramid Pooling Fast (SPPF) module to Spatial Pyramid Pooling Fast with Attention (SPPF-Attention) in the backbone, and adopting the Wise Intersection over Union (Wise-IoU) loss function. Subsequently, a posterior verification mechanism based on spatial constraint error is designed to eliminate unreliable detections by leveraging the inherent geometric priors of fasteners. Finally, the deflection angle, longitudinal displacement, and lateral displacement of the clip are calculated from the verified keypoints. Experimental results demonstrate that the proposed method achieves an Average Precision at IoU threshold from 0.5 to 0.95 (AP@0.5:0.95) of 77.5%, representing a 3.6% improvement over the baseline YOLOv8s-pose model, effectively balancing detection accuracy and computational efficiency. This work provides a reliable technical solution for the refined maintenance of rail fasteners.