Authors:
Yongzhi Min
1
;
Benyu Xiao
1
;
Hongfeng Ma
2
and
Biao Yue
1
Affiliations:
1
School of Automation and Electrical Engineering, Lanzhou Jiaotong University, China
;
2
School of Electronical Information Engineering, Lanzhou Institute of Technology, China
Keyword(s):
fastener missing detection, area positioning, template matching, occlusion removal, machine vision, minimum distance classifie
Abstract:
Rail fastener missing detection is an important part of railway daily inspection, according to the need of modern railway automatic detection, a method of rail fastener missing detection based on template matching is proposed in this paper. Firstly, in order to deal with the interference of environmental light, according to the basic principle of machine vision, a simple rail inspection car is designed for image acquisition. Secondly, according to the characteristics of the track image, the rail fastener area is located by using the mutation information of the image. Then, through the establishment of template, test images are matched with the template image, when the matching degree between test images and template images is low, it is need to detect the occlusion area of the test image and if there is a occlusion in the test image, remove the occlusion area from the test image and sample images to obtain new sample images and test image. Finally, the minimum distance classifier is
used to detect the missing rail fastener. Simulation results show that the correct detection rate of this algorithm is 93.7% and the average detection time of each image is 385.74 ms, providing a reference for real-time detection of railway line.
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