Deep Learning and Laser-Based 3-D Pixel-Level Rail Surface Defect Detection Method

Abstract

Rail surface defect inspection is of particular importance in modern railways. Accurate and efficient surface defect detection approaches support optimized maintenance. This enables safe operation of the railway network. However, the scale and harsh working environments of the railway still pose challenges to existing manual and vision-based inspection methods. Inspired by recent advances in laser measurement and deep learning in computer vision, this paper proposes a laser-based 3D pixel-level rail surface defect detection method that combines high-precision laser measurement data with the concept of deep semantic segmentation. In the proposed method, the rail surface is first measured in 3D using a low-cost 2D laser triangulation sensor. Then, a new deep semantic segmentation network is introduced. The network is composed of a fully convolutional segmentation module and two symmetric mapping modules, which can take 3D laser measurement data as input and output 3D pixel-level defect detection results in an end-to-end manner. The modular design of the network allows the use of various segmentation modules for different applications or scenarios. Experiments on a 3D rail dataset demonstrate the feasibility of the proposed method with a pixel-level detection accuracy measured by mean Intersection over Union (mIoU) of up to 87.9%. The 3D output provides not only location and boundary information but also the 3D characterization of defects, giving an essential reference for further defect management and repair tasks.