NDLI: High-speed Railway Fastener Detection Using Minima Significant Region and Local Binary Patterns

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High-speed Railway Fastener Detection Using Minima Significant Region and Local Binary Patterns

Content Provider	Scilit
Author	Fan, Hong Xiong, Ying Fei, Yaodong
Copyright Year	2019
Description	Journal: Journal of Physics: Conference Series Railway fastener is an important part of the railway system. Keeping the fasteners effective is essential to ensure the safe operation of the railway, so abnormal railway fastener detection is a main task of railway maintenance. With the development of railway system, the traditional manual fastener detection method has been unable to meet the application requirements, because it is very slow, costly, and dangerous. In this paper, we propose a novel method for abnormal fastener detection based on computer vision, which can detect missing fasteners automatically. In this method, Minima Significant Region is extracted in order to improve the fastener localization accuracy. Then, fastener recognition is operated using local binary features and Support Vector Machine classifier based on the fastener sub-images which are obtained by fastener localization. The proposed method is evaluated in our own database which is obtained by railway inspection system in different environments. The experimental results have shown improved performance against the state-of-the-art algorithm.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1302/4/042046/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1302/4/042046
Journal	Journal of Physics: Conference Series
Issue Number	4
Volume Number	1302
Language	English
Publisher	IOP Publishing
Publisher Date	2019-08-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Transportation Science and Technology Local Binary Fastener Detection Fastener Localization Railway System
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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