Open AccessA surface defect detection system for railway track based on machine vision
Author(s) -
Kang Zhao,
Laizhen Luo,
Zhengmin Ren,
Qingchen Fu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1678/1/012002
Subject(s) - computer vision , artificial intelligence , computer science , machine vision , matlab , edge detection , compensation (psychology) , track (disk drive) , feature extraction , upload , feature (linguistics) , image quality , image processing , enhanced data rates for gsm evolution , image (mathematics) , psychology , linguistics , philosophy , psychoanalysis , operating system
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.