Automatic Identification and Early-warning System for On-board Rail Corrugation Based on Image Processing Technology

Abstract

[Objective] Current daily metro track inspections, including manual detection and track inspection vehicle method, suffer from issues such as low efficiency and high costs, failing to meet the increasing demands for rail transit operation safety. Therefore, it is necessary to develop an automatic identification and early-warning system for on-board rail corrugation based on image processing technology. [Method] The overall solution for automatic rail corrugation identification is elucidated, covering the denoising and uneven illumination correction of rail surface images, the positioning and segmentation of rail surface images, the localization of rail corrugation interval, and the estimation of rail corrugation cycles. The automatic rail corrugation identification and assessment method based on image processing is elaborated. The construction of the defect automatic identification and early-warning system is described from both software and hardware perspective based on system composition and logic architecture, and the demonstration application situation of the system in Beijing Subway is introduced. [Result & Conclusion] The system enables real-time detection, localization, assessment and early-warning of rail corrugation defects. Demonstrative application test results indicate that the system achieves an identification accuracy of over 97% for rail corrugation defects. In comparison to conventional manual inspection and dedicated track inspection vehicles, the system exhibits a significant advantage in detection efficiency.

Keywords

• metro vehicle
• rail corrugation
• image processing
• automatic recognition