Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing

Qiu Yijin; Lv Zhaomin

doi:doi:10.11648/j.sd.20190706.19

| Peer-Reviewed

Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing

Qiu Yijin, Lv Zhaomin

Published in Science Discovery (Volume 7, Issue 6)

Received: 3 November 2019 Published: 12 December 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

With the rapid development of rail transit, the detection requirements for the various components of the track line are getting higher and higher, and relying on manual detection has the disadvantages of high cost and low efficiency. Therefore, it is urgent to study the method of automatic detection of track lines.This paper is based on the development history of computer vision and deep learning detection algorithm in fastener detection. It mainly introduces the related algorithms of positioning and classification, including the "cross" and template matching positioning algorithm; extracting the image direction gradient histogram The graph and the local binary pattern feature are merged, and the algorithm is classified by the support vector machine. At the same time, the convolutional neural network Alexnet architecture is used to extract the generalization characteristics of the fasteners to improve the classification accuracy of the fasteners. Finally, the problems and dilemmas of the existing fastener detection algorithms are discussed.

Published in	Science Discovery (Volume 7, Issue 6)
DOI	10.11648/j.sd.20190706.19
Page(s)	429-435
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Fastener Positioning, Fastener Classification, Deep Learning, Fastener Detect

References

[1]	王强,李柏林,侯云,范宏.一种改进的LBP特征实现铁路扣件识别[J].西南交通大学学报,2018,53(05):893-899.
[2]	王开雄,何彪,李柏林, 等.结合掩膜和可变形部件模型的扣件定位算法[J].计算机工程与应用,2019.
[3]	罗建桥,刘甲甲,李柏林, 等.基于局部特征和语义信息的扣件图像检测[J].计算机应用研究,2016,33(8):2514-2518,2523.
[4]	Zhang H B, Yang J F, Tao W, et al. Vision method of inspecting missing fastening components in highspeed railway [J]. Applied Optics, 2011, 50 (20): 3658-3665.
[5]	Yang Jinfeng, Tao Wei, Liu Manhua, et al. An efficient direction field-based method for the detection of fasteners on high-speedrailways [J]. Sensors, 2011, 11 (8): 7364 -7381.
[6]	Marino F, Distante A, Mazzeo P L, et al. A real-time visual inspection system for railway maintenance:automatic hexagonal-headed bolts detection [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 2007, 37 (3): 418-428．
[7]	吴禄慎, 万超, 陈华伟, 等. 一种改进的十字交叉轨道扣件定位方法[J]. 铁道标准设计, 2016(12):49-53.
[8]	邹逸,顾桂梅,张军平.基于改进Canny算子的铁路扣件定位方法[J].兰州交通大学学报,2019,38(01):72-77+94.
[9]	范宏,侯云,李柏林,熊鹰.高铁扣件的自适应视觉检测算法[J/OL].西南交通大学学报:1-6[2019-11-01].
[10]	谢凤英,吴叶芬,周世新.基于互信息的铁路轨枕扣件自动定位算法[J].中国体视学与图像分析,2013,18(02):145-149.
[11]	代国忠.图像识别技术在铁轨扣件异常检测中的应用研究[D].哈尔滨工程大学,2018.
[12]	李永波,李柏林,熊鹰.基于HOG特征的铁路扣件状态检测[J].传感器与微系统,2013,32(10):110-113.
[13]	韩金岳. 基于多特征融合的轨道螺栓扣件图像识别技术研究[D].兰州交通大学,2018.

Cite This Article

Plain Text BibTeX RIS

APA Style

Qiu Yijin, Lv Zhaomin. (2019). Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing. Science Discovery, 7(6), 429-435. https://doi.org/10.11648/j.sd.20190706.19

Copy | Download

ACS Style

Qiu Yijin; Lv Zhaomin. Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing. Sci. Discov. 2019, 7(6), 429-435. doi: 10.11648/j.sd.20190706.19

Copy | Download

AMA Style

Qiu Yijin, Lv Zhaomin. Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing. Sci Discov. 2019;7(6):429-435. doi: 10.11648/j.sd.20190706.19

Copy | Download

@article{10.11648/j.sd.20190706.19,
  author = {Qiu Yijin and Lv Zhaomin},
  title = {Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing},
  journal = {Science Discovery},
  volume = {7},
  number = {6},
  pages = {429-435},
  doi = {10.11648/j.sd.20190706.19},
  url = {https://doi.org/10.11648/j.sd.20190706.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20190706.19},
  abstract = {With the rapid development of rail transit, the detection requirements for the various components of the track line are getting higher and higher, and relying on manual detection has the disadvantages of high cost and low efficiency. Therefore, it is urgent to study the method of automatic detection of track lines.This paper is based on the development history of computer vision and deep learning detection algorithm in fastener detection. It mainly introduces the related algorithms of positioning and classification, including the "cross" and template matching positioning algorithm; extracting the image direction gradient histogram The graph and the local binary pattern feature are merged, and the algorithm is classified by the support vector machine. At the same time, the convolutional neural network Alexnet architecture is used to extract the generalization characteristics of the fasteners to improve the classification accuracy of the fasteners. Finally, the problems and dilemmas of the existing fastener detection algorithms are discussed.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing
AU  - Qiu Yijin
AU  - Lv Zhaomin
Y1  - 2019/12/12
PY  - 2019
N1  - https://doi.org/10.11648/j.sd.20190706.19
DO  - 10.11648/j.sd.20190706.19
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 429
EP  - 435
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20190706.19
AB  - With the rapid development of rail transit, the detection requirements for the various components of the track line are getting higher and higher, and relying on manual detection has the disadvantages of high cost and low efficiency. Therefore, it is urgent to study the method of automatic detection of track lines.This paper is based on the development history of computer vision and deep learning detection algorithm in fastener detection. It mainly introduces the related algorithms of positioning and classification, including the "cross" and template matching positioning algorithm; extracting the image direction gradient histogram The graph and the local binary pattern feature are merged, and the algorithm is classified by the support vector machine. At the same time, the convolutional neural network Alexnet architecture is used to extract the generalization characteristics of the fasteners to improve the classification accuracy of the fasteners. Finally, the problems and dilemmas of the existing fastener detection algorithms are discussed.
VL  - 7
IS  - 6
ER  -

Copy | Download

Author Information

Qiu Yijin

School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, China
Lv Zhaomin

School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Qiu Yijin, Lv Zhaomin. (2019). Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing. Science Discovery, 7(6), 429-435. https://doi.org/10.11648/j.sd.20190706.19

Copy | Download

ACS Style

Qiu Yijin; Lv Zhaomin. Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing. Sci. Discov. 2019, 7(6), 429-435. doi: 10.11648/j.sd.20190706.19

Copy | Download

AMA Style

Qiu Yijin, Lv Zhaomin. Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing. Sci Discov. 2019;7(6):429-435. doi: 10.11648/j.sd.20190706.19

Copy | Download

@article{10.11648/j.sd.20190706.19,
  author = {Qiu Yijin and Lv Zhaomin},
  title = {Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing},
  journal = {Science Discovery},
  volume = {7},
  number = {6},
  pages = {429-435},
  doi = {10.11648/j.sd.20190706.19},
  url = {https://doi.org/10.11648/j.sd.20190706.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20190706.19},
  abstract = {With the rapid development of rail transit, the detection requirements for the various components of the track line are getting higher and higher, and relying on manual detection has the disadvantages of high cost and low efficiency. Therefore, it is urgent to study the method of automatic detection of track lines.This paper is based on the development history of computer vision and deep learning detection algorithm in fastener detection. It mainly introduces the related algorithms of positioning and classification, including the "cross" and template matching positioning algorithm; extracting the image direction gradient histogram The graph and the local binary pattern feature are merged, and the algorithm is classified by the support vector machine. At the same time, the convolutional neural network Alexnet architecture is used to extract the generalization characteristics of the fasteners to improve the classification accuracy of the fasteners. Finally, the problems and dilemmas of the existing fastener detection algorithms are discussed.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing
AU  - Qiu Yijin
AU  - Lv Zhaomin
Y1  - 2019/12/12
PY  - 2019
N1  - https://doi.org/10.11648/j.sd.20190706.19
DO  - 10.11648/j.sd.20190706.19
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 429
EP  - 435
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20190706.19
AB  - With the rapid development of rail transit, the detection requirements for the various components of the track line are getting higher and higher, and relying on manual detection has the disadvantages of high cost and low efficiency. Therefore, it is urgent to study the method of automatic detection of track lines.This paper is based on the development history of computer vision and deep learning detection algorithm in fastener detection. It mainly introduces the related algorithms of positioning and classification, including the "cross" and template matching positioning algorithm; extracting the image direction gradient histogram The graph and the local binary pattern feature are merged, and the algorithm is classified by the support vector machine. At the same time, the convolutional neural network Alexnet architecture is used to extract the generalization characteristics of the fasteners to improve the classification accuracy of the fasteners. Finally, the problems and dilemmas of the existing fastener detection algorithms are discussed.
VL  - 7
IS  - 6
ER  -

Copy | Download