Railway Insulator Defect Detection with Deep Convolutional Neural Networks

摘要

Railway patrolling inspection train has been widely used for railway infrastructure safety monitoring. Cameras are mounted on the train, which can capture the image of the overhead contact power line system for defect detection. In the catenary support device of overhead contact power line system, the insulator can keep the catenary equipment insulated from other equipment. Defect detection of insulators is extremely important to railway safety. In recent years, some achievements have been made in defect detection on railway system based on computer vision. We propose an insulator localization algorithm and insulator defect detection algorithm using deep convolutional neural networks. Firstly, the insulator localization network based on Rotation Region Proposal Network (RRPN) can be used to locate insulator area in catenary support device images by using rotated bounding box. Rotated bounding box can effectively eliminate unnecessary background in localization results. After that, based on the insulator localization results, a Faster R-CNN based insulator defect detection network was used to detect defect of insulator. This method can effectively detect defect of insulator and solve the high false positive defect problem.