关键词: 实时检测, 机器视觉, 语义分割, 钢轨缺陷检测系统

Abstract:

Rail surface defect detection is the important part of railway daily detection. According to the requirements of modern railway automatic detection technology for real-time detection and adaptability, a complete system of rail surface defect identification and analysis is constructed. According to the basic principle of machine vision, an image acquisition device with LED auxiliary light source and light shielding box is designed, and the collected image is manually labeled, and a relatively large rail surface defect data set with semantic segmentation annotation is established. The advanced semantic segmentation technology is applied to rail image analysis. Using a cascaded self coding structure（CASAE） semantic segmentation network, the defect image is transformed into a pixel level prediction mask based on semantic segmentation, and the segmentation results are classified by the compact Convolutional Neural Network（CNN）, so as to realize the recognition and classification of rail surface defects. An intelligent human-computer interaction system is built and tested by simulation experiment. The experimental results show that the detection accuracy of the system is more than 90%, and the average processing time of each image is 245.61 ms, which can replace the manual detection to some extent, and realize the digital management of rail defects.

Key words: real time detection, machine vision, semantic segmentation, rail defect detection system