Collaborative Learning Attention Network Based on RGB Image and Depth Image for Surface Defect Inspection of No-Service Rail

Abstract

Surface defect inspection of no-service rail is important for safety of railway transportation. However, there are several challenges of irregular defect boundary, similar foreground and background for no-service rail surface defect inspection. To deal with the above challenges, depth image is used to provide complementary spatial information to RGB image. In recent years, with the development of deep learning and computer vision technology, intelligent inspection of defect has made great progress. We propose a neural network named collaborative learning attention network (CLANet) for no-service rail surface defect inspection. Our method can inspect the defect object of rail surface and segment the accurate region of that defect. The proposed method consists of three main stages: feature e