A Large-Model-Enhanced Method for Rail Surface Defect Detection in Heavy-Haul Railway

The rail surface defects directly impact the safety and efficiency of heavy-haul train operations. Timely assessment of these defects is crucial for informed maintenance decisions, with precise defect detection at its core. In recent years, the accumulation of extensive rail inspection images has led to the application of numerous computer vision-based methods for pixel-level detection of rail surface defects. However, given the constraint of a limited number of labeled defect samples, ensuring the generalization and robustness of existing methods remains challenging, particularly across varying track conditions and complex heavy-haul scenarios. Thus, this paper introduces a Segment-Anything-Model (SAM)-enhanced method for the detection of rail surface defects. First, a shadow-detection-based algorithm is developed to extract the rail regions and mitigate background interference. Then a student-teacher-Simi-network (S-T-Simi)-based unsupervised method is designed to generate prompt information for SAM. Utilizing this prompt information, we develop a task-specified SAM for precise rail defect detection. Finally, comprehensive validation is performed using inspection data collected from diverse heavy-haul tracks. Experimental results indicate that the proposed method achieves highly accurate segmentation of rail defects.