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Rail inspection based on visual inspection system (VIS) has drawn much attention recently, since VIS is automatic, fast, nondestructive, and objective. However, visual rail inspection is still a challenge to accurately identify possible defects involving a large variety of visual appearances. This paper presents an automatic visual inspection system to address these difficulties. Rail surface images are first obtained by the system, and then fed into the proposed hierarchical inspection framework containing coarse extractor and fine extractor. Specifically, the coarse extractor handles this defect inspection problem by exploring characteristics of rail background instead of defect itself, and focuses on finding the background modes. The fine extractor, which integrates the longitudinal context information and transversal prior information, is presented to largely reduce the impacts of other irregulars. The proposed method is evaluated on labeled Type-I and Type-II rail surface discrete defects data sets and a practical rail line. The experimental results demonstrate that our method outperforms the state-of-the-art works.
Gan et al. (Tue,) studied this question.