Online Rail Surface Inspection Utilizing Spatial Consistency and Continuity

Abstract

Rail surface inspection using visual inspection system is an important part of railway maintenance. However, accurate and efficient identification of possible defects remains challenging. This paper proposes a background-oriented defect inspector (BODI) to improve defect detection by considering specified characteristics of the track during inspection. Reformulating the inspection task in this manner offers a new way to model rail surface images. More specifically, BODI features a random sampling stage to obtain a compact background representation without any prior information. A sufficient number of random selections generates adequate and diverse background statistics, and defect-determination and a fusion of procedures then determine whether current pixel belongs to the background. Finally, a background update mechanism and parallelism ensure real-time applicability. The proposed BODI is evaluated on a working railway line. The experimental results demonstrate that it outperforms state-of-the-art methods.