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Combined PCA and NB to Predict Traffic Incident Duration Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Overview of Non-contact Pantograph-Catenary Arc Detection Based on Image Processing

Authors : Shize Huang, Fan Zhang, Liangliang Yu, Meiyu Pan

Published in: International Symposium for Intelligent Transportation and Smart City (ITASC) 2017 Proceedings

Publisher: Springer Singapore

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Abstract

The pantograph-catenary system of high-speed EMU is the only way to get the power of high-speed trains. Pantograph-catenary arc fault is a kind of common fault of pantograph-catenary system, which brings fatal harm for train and becomes an important factor to hinder the stable operation of the train. The pantograph-catenary arc detection based on image processing can detect the state of pantograph-catenary system quickly, which can judge whether there is an arc in system and maintain the safety of train. Compared with the traditional detection method, it will be divided into infrared, ultraviolet and visible light accordance with the detection of the use of image information, which on the basis of summarizing all kinds of non-contact arc detection methods in recent years. The edge detection, threshold segmentation and other algorithms are summarized and the characteristics of pantograph-catenary arc are highlighted by using image enhancement technology. In view of the shortcomings of the existing non-contact pantograph-catenary arc detection, the future development is prospected.

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previous chapter “Four-A-Services” Oriented Evaluated System of Intelligent City
next chapter Research on the Arc Image Recognition Based on the Pantograph Videos of High-Speed Electric Multiple Unit (EMU)
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Metadata
Title
Overview of Non-contact Pantograph-Catenary Arc Detection Based on Image Processing
Authors
Shize Huang
Fan Zhang
Liangliang Yu
Meiyu Pan
Copyright Year
2017
Publisher
Springer Singapore
Book
International Symposium for Intelligent Transportation and Smart City (ITASC) 2017 Proceedings

Print ISBN: 978-981-10-3574-6

Electronic ISBN: 978-981-10-3575-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-3575-3_28

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