The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-making, computer vision-based rail corrugation identification and assessment methods are presented in this paper. Firstly, an improved Spatial Pyramid Matching (SPM) model, integrating multi-features and locality-constrained linear coding (IMFLLC), is proposed for rail corrugation identification. After that, an innovative period estimation method for rail corrugation is proposed based on the frequency domain analysis of each column in the corrugation region. Finally, the severity of the rail corrugation is assessed with the help of the wear saliency calculation and fuzzy theory. The experiment results demonstrate that the proposed corrugation identification method achieves a higher precision rate and recall rate than those of traditional methods, reaching 99.67% and 98.34%, respectively. Besides, the validity and feasibility of the proposed methods for the rail corrugation period estimation and severity assessment are also investigated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.