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.
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