In this project, the detecting bolt loosening technology uses the CMOS camera for image acquisition, and the image is extracted by the FPGA combined with the single-chip computer. The author calculates the relative rotation angle of the bolt mark symbol before and after loosening by image analysis technology, through which we can quantify the loose angle of bolt and judge bolt’s loose condition. Then, the author uses the bolt screw maintenance machine to screw the loose bolt, which can achieve the purpose of repairing the loose bolt. This paper is of great significance in providing a theoretical basis for the theoretical design and practical engineering application of the rail maintenance machine.

No abstract

In order to detect rail surface defects more quickly and effectively, a rail surface defect detection system based on machine vision was constructed. A perfect lighting system is built to obtain high quality image information. At the same time, the linear CCD camera is used to collect the rail information, which is uploaded by FPGA, and the image information is processed and analyzed by MATLAB software in the upper computer. Through the methods of image denoising and image enhancement, the image quality is improved, and the image information suitable for operation is obtained; then, the rail positioning is completed by threshold segmentation, gray compensation and image binarization to simplify the image operation; finally, the final positioning of defects is realized through edge detection and feature extraction. By building a test platform on the rail maintenance vehicle, the rail surface defect information collected by the rail maintenance vehicle is analyzed and processed.