Deep learning-based detection technology for railway track surface defects
TL;DR
Performance evaluation on the dataset and ablation experiments verify that the proposed YOLOv11-WD model significantly improves detection accuracy while achieving a preliminary lightweight design compared with the baseline YOLOv11s.
Abstract
Railway tracks are essential to the transportation system and play a crucial role in rail transit operation. However, during long-term operation, defects such as cracks, wear, deformation, and corrosion are prone to occur on the rail surface. These defects may lead to train derailment or equipment failure, thereby causing serious safety accidents. To address these issues, this paper proposes a railway track surface defect recognition method based on an improved YOLOv11 model, aiming to achieve high-precision and real-time detection. In the neck network, a fused weighted bidirectional feature pyramid network is introduced, enabling the model to adaptively learn and adjust feature fusion weights. In the backbone network, a DSConv-C3K2 dynamic snake convolution enhancement module is designed to better capture slender and irregular defect features. Performance evaluation on the dataset and ablation experiments verify that the proposed YOLOv11-WD model significantly improves detection accuracy while achieving a preliminary lightweight design compared with the baseline YOLOv11s. In addition, the improved modules demonstrate a clear synergistic effect. For the recognition of four types of railway track defects, the baseline YOLOv11s model achieves an mAP@0.5 of 93.5%, while the final improved model reaches 96.4%, marking a 2.9 percentage point improvement over the original YOLOv11s.