A Novel Decomposition Model for Visual Rail Surface Inspection

Ziwen Zhang,Zhiyu Liu,Mangui Liang

doi:10.3390/electronics10111271

Abstract

Rail surface inspection plays a pivotal role in large-scale railway construction and development. However, accurately identifying possible defects involving a large variety of visual appearances and their dynamic illuminations remains challenging. In this paper, we fully explore and use the essential attributes of our defect structure data and the inherent temporal and spatial characteristics of the track to establish a general theoretical framework for practical applications. As such, our framework can overcome the bottleneck associated with machine vision inspection technology in complex rail environments. In particular, we consider a differential regular term for background rather than a traditional low-rank constraint to ensure that the model can tolerate dynamic background changes without losing sensitivity when detecting defects. To better capture the compactness and completeness of a defect, we introduce a tree-shaped hierarchical structure of sparse induction norms to encode the spatial structure of the defect area. The proposed model is evaluated with respect to two newly released Type-I/II rail surfaces discrete defects (RSDD) data sets and a practical rail line. Qualitative and quantitative evaluations show that the decomposition model can handle the dynamics of the track surface well and that the model can be used for structural detection of the defect area.

Highlights

With progressively large-scale high-speed railway construction and increasingly rapid development, ensuring safe service is becoming ever more important
Using principal component pursuit (PCP) with1 norm constraints, the detection of defect components is scattered, resulting in fragmented detection, whereas the defects separated by our model based on the structural sparsity constraint and the DECOLORp with MRF smoothing constraints (DEC) model based on the MRF smoothing constraint are relatively compact and complete
We have presented a novel decomposition model for rail defect inspection, assuming that the defected rail image can be represented as the superposition of background, defect, and noise components

Summary

Introduction

With progressively large-scale high-speed railway construction and increasingly rapid development, ensuring safe service is becoming ever more important. Some lines exhibit serious structural defects such as continuous fastener failure and broken rails because of their extreme operating environments. Failure to deal with these defects promptly will shorten the service life of railway facilities and directly affect the safety of line operations. To make up for the shortcomings of existing detection methods, the development of intelligent detection technologies has become a requirement for maintaining the safe and stable operation of high-speed railway lines [1]. With the continuous development of related theories and technologies such as image processing, machine learning, and artificial intelligence, research in computer vision is gradually shifting from theory to practical applications, to newly proposed vision detection models for specific applications, and to the development of corresponding algorithms

Methods

Discussion

Conclusion