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Journal of Zhejiang University SCIENCE A 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/jzus.A2100494


Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection


Author(s):  Ping TAN, Xu-feng LI, Jin DING, Zhi-sheng CUI, Ji-en MA, Yue-lan SUN, Bing-qiang HUANG, You-tong FANG

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

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

Key Words:  High speed railway catenary insulator, Mask R-CNN, multifeature fusion, K-means clustering analysis model, defect detection


Ping TAN, Xu-feng LI, Jin DING, Zhi-sheng CUI, Ji-en MA, Yue-lan SUN, Bing-qiang HUANG, You-tong FANG. Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .

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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2100494"
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A1 - Ji-en MA
A1 - Yue-lan SUN
A1 - Bing-qiang HUANG
A1 - You-tong FANG
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DOI - 10.1631/jzus.A2100494


Abstract: 
Rod insulators are vital parts of the catenary of high speed railways. The failure of an insulator will cause insulation deterioration, even power interruption of the catenary. Insulator defect detection is important for high speed train operation. The image quality of a high-speed rail camera of a catenary support device is poor. The catenary support device has many components and the background of the insulator image is complicated. It is difficult to recognise insulators and detect defects automatically. There are many different catenary insulators, and railway departments and companies lack reliable and universal defect detection methods. In this article, we propose a catenary intelligent defect detection algorithm based on mask R-CNN and an image processing model. Vertical projection technology is used to achieve single shed positioning and precise cutting of the insulator. Gradient, texture and gray feature fusion (GTGFF) and a k-means clustering analysis model (KCAM) are proposed to detect broken insulators, dirt, foreign bodies and flashover. Using this model, insulator recognition and defect detection can achieve a high recall rate and accuracy, and generalized defect detection. The algorithm was tested and verified on a dataset of realistic insulator images, and the accuracy and reliability of the algorithm satisfied current requirements for high speed railway catenary automatic inspection and intelligent maintenance.

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

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