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CLC number: TM216; TP311

On-line Access: 2020-01-04

Received: 2019-07-18

Revision Accepted: 2019-10-09

Crosschecked: 2019-12-12

Cited: 0

Clicked: 2505

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ping Tan

https://orcid.org/0000-0001-8656-3514

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Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.1 P.64-73

http://doi.org/10.1631/jzus.A1900341


Catenary insulator defect detection based on contour features and gray similarity matching


Author(s):  Ping Tan, Xu-feng Li, Jin-mei Xu, Ji-en Ma, Fei-jie Wang, Jin Ding, You-tong Fang, Yong Ning

Affiliation(s):  School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; more

Corresponding email(s):   majien@zju.edu.cn

Key Words:  High speed railway insulator, Defect detection, Contour extraction, Shed separation, Gray similarity


Ping Tan, Xu-feng Li, Jin-mei Xu, Ji-en Ma, Fei-jie Wang, Jin Ding, You-tong Fang, Yong Ning. Catenary insulator defect detection based on contour features and gray similarity matching[J]. Journal of Zhejiang University Science A, 2020, 21(1): 64-73.

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author="Ping Tan, Xu-feng Li, Jin-mei Xu, Ji-en Ma, Fei-jie Wang, Jin Ding, You-tong Fang, Yong Ning",
journal="Journal of Zhejiang University Science A",
volume="21",
number="1",
pages="64-73",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1900341"
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%T Catenary insulator defect detection based on contour features and gray similarity matching
%A Ping Tan
%A Xu-feng Li
%A Jin-mei Xu
%A Ji-en Ma
%A Fei-jie Wang
%A Jin Ding
%A You-tong Fang
%A Yong Ning
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1900341

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T1 - Catenary insulator defect detection based on contour features and gray similarity matching
A1 - Ping Tan
A1 - Xu-feng Li
A1 - Jin-mei Xu
A1 - Ji-en Ma
A1 - Fei-jie Wang
A1 - Jin Ding
A1 - You-tong Fang
A1 - Yong Ning
J0 - Journal of Zhejiang University Science A
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SP - 64
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%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1900341


Abstract: 
Insulators are the key components of high speed railway catenaries. Insulator failures can cause outages and affect the safe operation of high speed railways. It is important to perform insulator defect detection. Due to the collection of insulator images by moving catenary inspection vehicles, the consistency of the images is poor, and the number of insulator defect samples is very small. An algorithm of deep learning and conventional template matching cannot meet the requirements of insulator defect detection. This paper proposes a fusion algorithm based on the shed contour features and gray similarity matching. High accuracy and consistency of contour extraction and precise separation of each insulator shed were realized. An insulator defect detection model based on the spacing distance of the sheds and the gray similarity was constructed. Experiments show that the method based on the contour features and gray similarity matching can effectively classify normal insulators and defective insulators. Recall of 99.50% and high precision of 91.71% were achieved in the test of the image data set, and this can meet the requirements for the reliability and high precision of a detection algorithm for catenary insulators.

The authors propose fusion algorithm based on the shed contour extraction and gray similarity matching, which is of great significance to the high-speed railway network. The paper is well organized and clearly stated.

基于轮廓特征及灰度相似度匹配的接触网绝缘子缺陷检测

目的:在图像缺陷样本少和一致性差的情况下,实现精确可靠的接触网绝缘子缺陷检测.
创新点:提出一种基于瓷片轮廓特征及灰度相似度匹配的融合算法,实现了绝缘子瓷片的轮廓提取及绝缘子各瓷片的精准分离,并构建了基于瓷片间距和灰度相似度匹配的绝缘子缺陷检测模型.
方法:1. 采用同一个绝缘子相邻瓷片两两比较的方法进行缺陷检测,解决图像缺陷样本少和一致性差的问题. 2. 分两步进行检测(Fig. 2):(1)基于水平梯度特征提取绝缘子各瓷片轮廓,并对瓷片轮廓内像素进行复原; (2)计算瓷片间距和灰度相似度,并与设置的阈值进行比较以区分正常绝缘子和缺陷绝缘子.
结论:1. 实验表明,基于轮廓特征及灰度相似度匹配的方法能够有效区分正常绝缘子和缺陷绝缘子. 2. 在图片数据集中,测试达到了99.50% 的高召回率和91.71%的高精确度,满足了目前较高水平的接触网绝缘子缺陷检测的要求.

关键词:高铁绝缘子; 缺陷检测; 轮廓提取; 瓷片分离; 灰度相似度

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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