Research on Detection Algorithm for Rail Fastener Based on Computer Vision

DOI

10.2991/mecae-18.2018.115How to use a DOI?

Keywords

Computer Vision; Rail Fastener; HOG Features; Nearest Neighbor Classifier

Abstract

The traditional rail detection method cannot meet the demand of line repair, so a detection algorithm for rail fastener based on computer vision is proposed, which combines projection method and scanning of pixels in specific region to position the position of fastener, and adopts gray-scale features and HOG features to describe feature vector of fastener, then uses Chi square distance classifier to extract features. The experimental result shows the algorithm is effective and feasible to a certain extent.

Copyright

© 2018, 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/).

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TY  - CONF
AU  - Xin Liu
AU  - Hongbin Wang
AU  - Bin Zhou
PY  - 2018/03
DA  - 2018/03
TI  - Research on Detection Algorithm for Rail Fastener Based on Computer Vision
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SP  - 371
EP  - 376
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.115
DO  - 10.2991/mecae-18.2018.115
ID  - Liu2018/03
ER  -