Depth Repeated-Enhancement RGB Network for Rail Surface Defect Inspection
Junyi Wu, Wujie Zhou, Weiwei Qiu, Lu Yu
Abstract
Surface defect inspection of railways is important to ensure safe transportation. However, challenging conditions, such as uneven illumination and similar foreground and background, hinder defect inspection. With the development of deep learning and the wide application of the computer vision, defect inspection has made great progress. Accordingly, we propose a depth repeated-enhancement RGB (red–green–blue) network (DRERNet) for rail surface defect inspection. DRERNet fully uses depth and RGB information to better inspect defects on rail surfaces using an encoder–decoder architecture. In the encoder, a novel cross modality enhancement fusion module uses details from RGB maps and location information from depth maps to perform cross-modality fusion. In the decoder, the details and location information in a multimodality complementation module are repeatedly used to progressively refine the DRERNet prediction. We performed extensive experiments, and compared the proposed DRERNet with 10 state-of-the-art methods on the industrial NEU RSDDS-AUG RGB-depth dataset. The comparison results demonstrate that DRERNet consistently performs better than other methods in the all evaluation measures.