Railway insulator defect detection with deep convolutional neural networks

Zichen Gu; Yanguo Wang; Xiantang Xue; Shengchun Wang; Yu Cheng; Xinyu Du; Peng Dai

doi:10.1117/12.2572918

12 June 2020 Railway insulator defect detection with deep convolutional neural networks

Zichen Gu, Yanguo Wang, Xiantang Xue, Shengchun Wang, Yu Cheng, Xinyu Du, Peng Dai

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 1151903 (2020) https://doi.org/10.1117/12.2572918
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Railway patrolling inspection train has been widely used for railway infrastructure safety monitoring. Cameras are mounted on the train, which can capture the image of the overhead contact power line system for defect detection. In the catenary support device of overhead contact power line system, the insulator can keep the catenary equipment insulated from other equipment. Defect detection of insulators is extremely important to railway safety. In recent years, some achievements have been made in defect detection on railway system based on computer vision. We propose an insulator localization algorithm and insulator defect detection algorithm using deep convolutional neural networks. Firstly, the insulator localization network based on Rotation Region Proposal Network (RRPN) can be used to locate insulator area in catenary support device images by using rotated bounding box. Rotated bounding box can effectively eliminate unnecessary background in localization results. After that, based on the insulator localization results, a Faster R-CNN based insulator defect detection network was used to detect defect of insulator. This method can effectively detect defect of insulator and solve the high false positive defect problem.

Citation Download Citation

Zichen Gu, Yanguo Wang, Xiantang Xue, Shengchun Wang, Yu Cheng, Xinyu Du, and Peng Dai "Railway insulator defect detection with deep convolutional neural networks", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 1151903 (12 June 2020); https://doi.org/10.1117/12.2572918

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Defect detection

Detection and tracking algorithms

Inspection

Convolutional neural networks

Cameras

Image segmentation

Inspection equipment

Show All Keywords

Keywords/Phrases

Search In:

Publication Years