Method for automatic railway track surface defect classification and evaluation using a laser‐based 3D model. Issue 12 (8th September 2020)

• Record Type: Journal Article
• Authors: Ye, Jiaqi; Stewart, Edward; Zhang, Dingcheng; Chen, Qianyu; Roberts, Clive
• Is Part Of: IET image processing. Volume 14:Issue 12(2020)
• Page Start: 2701
• Page End: 2710
• Publication Date: 2020-09-08
• Subjects: flaw detection -- image classification -- artificial intelligence -- rails -- mechanical engineering computing -- railways -- maintenance engineering -- automatic optical inspection -- railway engineering -- feature extraction -- pattern classification -- wheels; rail measurement data -- automatic railway track surface defect classification -- laser‐based 3D model -- physical surface defects -- wheels -- laser‐based optical inspection technologies -- 2D profile measurement -- rail maintenance -- artificial intelligence algorithms -- geometrical feature extraction -- multiclass classifiers; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1616
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser‐based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human‐dominated defect classification and evaluation work. This study presents a 3D model‐based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi‐class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model‐based automatic track surface defect inspection.