Region and Edge-Aware Network for Rail Surface Defect Segmentation

Rail surface defect segmentation can provide a reliable basis for the severity evaluation of rail diseases. The deep learning technology has been widely applied to segment rail surface defects due to the powerful feature representation ability. However, most of the existing deep learning-based methods often produce inaccurate defect region boundaries and unsatisfactory segmentation results since the inadequate integration of contextual information and insufficient edge features. To tackle this problem, a novel region and edge-aware network (REA-Net) is proposed for rail surface defect segmentation in this paper. REA-Net adopts an encoder-decoder framework, where the encoder network incorporates three modules: feature pyramid edge module, multi-task learning module, and cross-level fusion module, to encode richer context information and finer-grained features at each stage. The decoder network aggregates the encoding features from each stage to obtain decoding features, and final these features are used to predict the defect segmentation results. Extensive experiments are conducted on expanded public rail surface defect dataset (RSDD) and the results demonstrate our proposed REA-Net achieves good segmentation performance and outperforms the state-of-the-arts in terms of F ₁ -scores and IoU.