Research On Image Recognition Of The Train Bottom Faults

In recent years, with the rapid development of world’s high-speed railway, especially for the running of high-speed motor train units, rail safety inspection accept new challenge, more rigorous and sophisticated maintenance and monitoring of trains are put forward to implement. Because of complexity structures and so many small-parts, it is too hard and tired for works to accurately handle all problems. Any subtle fault could cause a serious accident in high speed. As intelligent automatic recognition technology, machine vision image processing technology which been widely used has great significance to realize railway vehicle safety inspection.Aiming at designing locomotive-bottom-image acquisition system and realizing faults automatic recognition to reduce man-made influence and improve detection efficiency, it is guided for the research of similar cases or automatic detection system.In the paper, on the premise of investigating and summarizing railway image detection technologies and devices and image processing technologies, locomotive-bottom image acquisition system is designed and set up. Combining with analysis of properties of image feature extraction methods and structure characteristics of train, five structure-segmentation rules are defined. Take the integration of rectangular function and gray projection to segment image, and then get the object-bolts image date based on the relationship between each structure-segmentation rule. A set of defects recognition algorithms are proposed based on filtering, histogram equalization, area thresh, connected region extraction, closed rectangular areas descriptors. Faults of bolts missing can be positioned accurately through using the proposed method in the testing image. Over all, it will give some guidelines for the work afterward.