Railway Fastener Defects Recognition Algorithm Based on Computer Vision

DOI

10.2991/esac-15.2015.69How to use a DOI?

Keywords

Railway fastener detection, Symmetrical image, Computer vision, Harr-like feature, Improved sparse representation

Abstract

Railway fastener detection is an important task in railway maintenance to ensure safety. However, the earlier detection methods based on computer vision have good performance on missing fasteners, but they have weaker ability to recognize the partially worn ones. In this paper, we exploit the axis-symmetrical structure to generate the first and second symmetry sample of original testing fastener image, and integrate the first and second image for improved representation-based fastener recognition. The underlying advantages of the scheme are as follows: first, the symmetry image can somewhat overcome the difficulty that the lack of training and testing samples. Second, the symmetry image is helpful for representation-based fastener recognition and we can obtain an accurate judgment of the original testing image by integrate the corresponding judgments of two symmetry image. The experiment results show that our proposed method can achieve a rather high precision.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Jiajia Liu
AU  - Bailin Li
PY  - 2015/08
DA  - 2015/08
TI  - Railway Fastener Defects Recognition Algorithm Based on Computer Vision
BT  - Proceedings of the 2015 International Conference on Electronic Science and Automation Control
PB  - Atlantis Press
SP  - 285
EP  - 288
SN  - 2352-538X
UR  - https://doi.org/10.2991/esac-15.2015.69
DO  - 10.2991/esac-15.2015.69
ID  - Liu2015/08
ER  -