High-speed Railway Fastener Detection Using Minima Significant Region and Local Binary Patterns

Abstract Railway fastener is an important part of the railway system. Keeping the fasteners effective is essential to ensure the safe operation of the railway, so abnormal railway fastener detection is a main task of railway maintenance. With the development of railway system, the traditional manual fastener detection method has been unable to meet the application requirements, because it is very slow, costly, and dangerous. In this paper, we propose a novel method for abnormal fastener detection based on computer vision, which can detect missing fasteners automatically. In this method, Minima Significant Region is extracted in order to improve the fastener localization accuracy. Then, fastener recognition is operated using local binary features and Support Vector Machine classifier based on the fastener sub-images which are obtained by fastener localization. The proposed method is evaluated in our own database which is obtained by railway inspection system in different environments. The experimental results have shown improved performance against the state-of-the-art algorithm.