Real time detection system for rail surface defects based on machine vision

The detection of rail surface defects is an important part of railway daily inspection, according to the requirements of modern railway automatic detection technology on real-time detection and adaptability. This paper presents a method for real-time detection of rail surface defects based on machin...

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Vydáno v:EURASIP journal on image and video processing Ročník 2018; číslo 1; s. 1 - 11
Hlavní autoři: Min, Yongzhi, Xiao, Benyu, Dang, Jianwu, Yue, Biao, Cheng, Tiandong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 11.01.2018
SpringerOpen
Témata:
Biometrics
Defects detection image enhancement
Direction chain code
Engineering
Image Processing and Computer Vision
Machine vision
Morphological processing
Pattern Recognition
Signal,Image and Speech Processing
Visual Information Learning and Analytics on Cross-Media Big Data
Direction chain code
Machine vision
Defects detection image enhancement
Morphological processing
ISSN:1687-5281, 1687-5281
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Shrnutí:The detection of rail surface defects is an important part of railway daily inspection, according to the requirements of modern railway automatic detection technology on real-time detection and adaptability. This paper presents a method for real-time detection of rail surface defects based on machine vision. According to the basic principle of machine vision, an image acquisition device equipped with LED auxiliary light source and shading box has been designed and the portable testing model is designed to carry on the field experiment. In view of the real-time requirement, the method of extracting the target area from the original image is carried out without image pre-processing. The surface defects of the rail are optimized based on morphological process and the characteristics of the defects are obtained by tracking the direction chain code. It is demonstrated that the maximum positioning time of this proposed method is 4.65 ms and its maximum positioning failure rate is 5%. The real-time detection speed of this proposed method can reach 2 m/s, which can carry out real-time detection of artificial hand walking. The time of processing each picture is up to 245.61 ms, which ensures the real-time performance of the portable track defect vision inspection system. To a certain extent, the system can replace manual inspection and carry out the digital management of track defects.
ISSN:1687-5281
1687-5281
DOI:10.1186/s13640-017-0241-y