Survey on vision-based railway track defect detection - CHEN Tianyan; HAN Zeming; HUANG Yunhu; SHI Jinjin; CHEN Dewang (智能科学与技术学报)
The detection of train track defects is of great significance to the safety of rail transportation, but current manual inspection can no longer meet the complex and heavy track inspection requirements. Deep learning methods have greatly expanded the means and detection capabilities of defect detection, and in order to improve the efficiency and quality of surface defect detection, the current trends of visual detection methods in conjunction with the types and related attributes of track defects was systematically analyzed. The basic principles, technologies, methods, and current application status of visual detection methods for track and fastening defects, as well as the concepts, applications, and significance of these detection methods were elaborated. Finally, the current trends in the field of track defect detection are analyzed and summarized, and for the first time proposes the concept of a fully automatic track maintenance system, which aims to provide useful support and reference for future related research.