关键词: 铁道工程；铁轨异物检测；SF-YOLO；轻量化检测；特征融合

Abstract: To address the issues of low detection accuracy of target and poor real-time performance caused by severe environmental interference in rail track foreign object detection under conditions such as rain, fog and low light, an improved YOLOv12 railway track intrusion foreign object detection algorithm (SF-YOLO) was proposed. Firstly, to address low detection accuracy under environmental interference, an enhanced multi-dimensional collaborative attention mechanism (MCAM) was employed, which enhanced feature expression through three-dimensional collaborative modelling. Secondly, to improve detection accuracy while maintaining real-time performance, a new lightweight feature fusion module, SFNet-Rail, was established, which preserved the details of foreign object boundaries while strengthening multi-scale feature consistency. Thirdly, a FastPartial-Detect detection head network based on partial convolutions (PConv) was designed to reduce memory access redundancy and further enhance the detection performance of the model. Finally, a unified IoU loss function (UIoU) was introduced to dynamically optimize the weight distribution of prediction boxes. Experimental results demonstrate that on the self-built rail track foreign object dataset, the proposed algorithm achieves a mean accuracy (PmA@0.5) of 85.8%, with a 2.7% improvement over YOLOv12. The detection speed of the proposed algorithm is increased by 12%, and the computational load is decreased by 21.25%. The accuracy and real-time performance of multi-scale railway foreign object detection in environments such as rain, fog, and low light conditions can be effectively enhanced.

Key words: railway engineering; railway track foreign object detection; SF-YOLO; lightweight detection; feature fusion