Foreign object intrusion in railway tracks is a major threat to train operation safety, yet current detection methods face challenges in identifying small distant targets and adapting to low-light conditions. Moreover, existing systems often lack the ability to assess intrusion risk levels, limiting real-time warning and graded response capabilities. To address these gaps, this paper proposes a novel method for intrusion detection and risk assessment based on keypoint spatial discrimination. First, an XS-BiSeNetV2-based track segmentation network is developed, incorporating cross-feature fusion and spatial feature recalibration to improve track extraction accuracy in complex scenes. Second, an enhanced STI-YOLO detection model is introduced, integrating a Shuffle attention mechanism for better feature interaction, a high-resolution Transformer detection head to improve small-target sensitivity, and the Inner-IoU loss function to refine bounding box regression. Detected targets’ bottom keypoints are then analyzed relative to track boundaries to determine intrusion direction. By combining lateral distance and motion state features, a multi-level risk classification system is established for quantitative threat assessment. Experiments on the RailSem19 and GN-rail-Object datasets show that the method achieves a track segmentation mIoU of 88.19% and a detection mAP of 82.6%. The risk assessment module effectively quantifies threats across scenarios and maintains stable performance under low-light and strong-glare conditions. This work offers a quantifiable risk assessment solution for intelligent railway safety systems.
Ning et al. (2026) studied this question.