Automatic pixel-level crack detection with multi-scale feature fusion for slab tracks

Cracks are common defects in slab tracks, which can grow and expand over time, leading to a deterioration of the mechanical properties of slab tracks and shortening service life. Therefore, it is essential to accurately detect and repair cracks before they impact services. This study developed a systematic pixel-level crack segmentation-quantification method suited for nighttime detection of slab tracks. To be specific, slab track crack network II, a pixel-level segmentation network that aggregates multi-scale information was proposed to extract the morphology of slab track cracks, and then their widths were calculated by an alternative quantification method proposed in the paper. The model performs best when the initial learning rate is 0.0001, with intersection over unions (IOUs) 84.94% and 83.84% observed on the training set and validation set, respectively. In the test set, the IOU value is 81.07%, higher than that derived from similar segmentation algorithms, indicating higher robustness and better generalization of the network architecture. In addition, the average errors in predicting crack widths resulting from the proposed method are 0.13 and 0.12 mm, compared to the results measured by a vernier caliper and a 3D scanner, respectively. The proposed pixel-level segmentation-quantification system provides a new method and theoretical support for slab track maintenance and repair.