Abnormal railway fastener detection using minimal significant regions and local binary patterns

Railway fasteners are an important part of the railway system. Keeping the fasteners effective is essential to ensuring the safe operation of railways, so abnormal railway fastener detection is an important task in railway maintenance. With the development of the 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 what we believe to be a novel method for abnormal fastener detection based on computer vision, which can detect missing and ectopic fasteners automatically. In this method, the minimal significant region is extracted in order to improve the fastener localization accuracy. Then, fastener recognition is operated using local binary features and a support vector machine classifier based on the fastener sub-images that are obtained by fastener localization. The proposed method is evaluated in our own database, which is obtained by a railway inspection system in different environments. The experimental results have shown improved performance against the state-of-the-art algorithm. (C) 2020 Optical Society of America