Research on Detection Algorithm for Rail Fastener Based on Computer Vision
- https://doi.org/10.2991/mecae-18.2018.115How to use a DOI?
- Computer Vision; Rail Fastener; HOG Features; Nearest Neighbor Classifier
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.
- © 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/).
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 - 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 - https://doi.org/10.2991/mecae-18.2018.115 ID - Liu2018/03 ER -