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A Topology Guided Method for Rail-Track Detection.
- Source :
- IEEE Transactions on Vehicular Technology; Feb2022, Vol. 71 Issue 2, p1426-1438, 13p
- Publication Year :
- 2022
-
Abstract
- Rail-track detection is a core function for automated rail transit perception. However, existing methods cannot effectively detect the rail-tracks in a complex environment, especially in turnout scenarios. In this study, we propose a topology guided method to detect rail-tracks which includes the following four parts: Firstly, a neural network is used to obtain the pixels of the rail-lanes, and the geometric relationship between rail-lanes is mined by inverse perspective transformation. Secondly, the rail-lanes’ pixels are converted to rail-lanes’ key points and the topological relationship between the key points. Thirdly, the rail-lanes are reconnected through topological relationships between key points. Finally, the rail-track geometry features are used to match the rail-lanes. Experimental results show that the rail-track level F1 score of the proposed method reached 91.62%, which is state-of-the-art (SOTA) in this field. Furthermore, the proposed method has been tested and applied on the Hong Kong Metro Tsuen Wan Line. [ABSTRACT FROM AUTHOR]
- Subjects :
- TOPOLOGY
RAILROADS
IMAGE reconstruction
IMAGE segmentation
PIXELS
Subjects
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 155334362
- Full Text :
- https://doi.org/10.1109/TVT.2021.3133327