Leveraging the object detection paradigm for temporal signals: a keypoint-guided approach to stage analysis for railway turnout system

Abstract As a critical component of railway transportation systems, the turnout plays a key role in ensuring the safety of train operations and enhancing transport efficiency. The current signal of a turnout provides critical insight for identifying anomalies and exhibits distinct stage characteristics during its operation. To automatically identify these stages for equipment condition monitoring as well as intelligent operation and maintenance, accurate boundary detection is indispensable. Thus, this paper proposes a keypoint-guided stage segmentation approach for current curve images of railway turnout system. The method performs stage segmentation using solely the top-left keypoint of each region, with adjacent keypoints defining stage boundaries, dramatically reducing model complexity. To enhance boundary supervision, an anisotropic Gaussian kernel is introduced during feature map generation, and a Sobel-based auxiliary gradient constraint is incorporated to improve detection precision. Furthermore, a temporal continuity constraint is applied in the post-processing stage to ensure logical consistency between consecutive segments. Experimental results demonstrate the robust performance of the proposed method, achieving precise segmentation of turnout current-curve images under both normal and abnormal conditions. Furthermore, it maintains low computational cost and ensures the rationality of the detection results.