In recent years, structured light measurement technology has been widely used in rail profile measurement. Due to the small depth of field of the conventional rail profile measurement with linear structured light, the imaging problem of defocusing blur occurs in the area of rail waist and rail bottom, which leads to low accuracy of rail profile measurement. Therefore, the Scheimpflug condition is applied to the rail profile measurement system with linear structured light, and a constant focus optical path for rail profile measurement is designed. The results show that compared with the conventional measurement optical path, the depth of field of the imaging system is expanded from less than 90 mm to 210 mm, the width of the light strip at the rail waist and rail bottom is reduced and the energy is more concentrated. At the same time, the clear imaging of the rail head, rail waist and rail bottom is ensured, and the rail profile measurement error is reduced from 0.094 mm to 0.071 mm. This method can solve the problem of low measurement accuracy caused by defocusing blur in traditional measurement optical path, and provide a reference for the application of Scheimpflug condition in rail profile measurement.
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.
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