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Graph-Embedded Lane Detection.
- Source :
- IEEE Transactions on Image Processing; 2021, Vol. 30, p2977-2988, 12p
- Publication Year :
- 2021
-
Abstract
- Lane detection on road segments with complex topologies such as lane merge/split and highway ramps is not yet a solved problem. This paper presents a novel graph-embedded solution. It consists of two key parts, a learning-based low-level lane feature extraction algorithm, and a graph-embedded lane inference algorithm. The former reduces the over-reliance on customized annotated/labeled lane data. We leveraged several open-source semantic segmentation datasets (e.g., Cityscape, Vistas, and Apollo) and designed a dedicated network that can be trained across these heterogeneous datasets to extract lane attributes. The latter algorithm constructs a graph to represent the lane geometry and topology. It does not rely on strong geometric assumptions such as lane lines are a set of parallel polynomials. Instead, it constructs a graph based on detected lane nodes. The lane parameters in the world coordinate are inferred by efficient graph-based searching and calculation. The performance of the proposed method is verified on both open source and our own collected data. On-vehicle experiments were also conducted and the comparison with Mobileye EyeQ2 shows favorable results. [ABSTRACT FROM AUTHOR]
- Subjects :
- FEATURE extraction
GRAPH algorithms
TOPOLOGY
REPRESENTATIONS of graphs
DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 30
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 170077681
- Full Text :
- https://doi.org/10.1109/TIP.2021.3057287