Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Research on Detection Algorithm for Rail Fastener Based on Computer Vision

Authors
Xin Liu, Hongbin Wang, Bin Zhou
Corresponding Author
Xin Liu
Available Online March 2018.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Publication Date
March 2018
ISBN
978-94-6252-493-4
DOI
https://doi.org/10.2991/mecae-18.2018.115How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/mecae-18.2018.115
DO  - https://doi.org/10.2991/mecae-18.2018.115
ID  - Liu2018/03
ER  -