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Authors: Nima Khairdoost ; Steven S. Beauchemin and Michael A. Bauer

Affiliation: Department of Computer Science, The University of Western Ontario, London, ON, N6A-5B7, Canada

Keyword(s): Lane Detection, Lane Type Classification, CNN.

Abstract: Road lane detection systems play a crucial role in the context of Advanced Driver Assistance Systems (ADASs) and autonomous driving. Such systems can lessen road accidents and increase driving safety by alerting the driver in risky traffic situations. Additionally, the detection of ego lanes with their left and right boundaries along with the recognition of their types is of great importance as they provide contextual information. Lane detection is a challenging problem since road conditions and illumination vary while driving. In this contribution, we investigate the use of a CNN-based regression method for detecting ego lane boundaries. After the lane detection stage, following a projective transformation, the classification stage is performed with a RseNet101 network to verify the detected lanes or a possible road boundary. We applied our framework to real images collected during drives in an urban area with the RoadLAB instrumented vehicle. Our experimental results show that our approach achieved promising results in the detection stage with an accuracy of 94.52% in the lane classification stage. (More)

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Paper citation in several formats:
Khairdoost, N.; Beauchemin, S. and Bauer, M. (2021). Road Lane Detection and Classification in Urban and Suburban Areas based on CNNs. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 450-457. DOI: 10.5220/0010241004500457

@conference{visapp21,
author={Nima Khairdoost. and Steven S. Beauchemin. and Michael A. Bauer.},
title={Road Lane Detection and Classification in Urban and Suburban Areas based on CNNs},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={450-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010241004500457},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Road Lane Detection and Classification in Urban and Suburban Areas based on CNNs
SN - 978-989-758-488-6
IS - 2184-4321
AU - Khairdoost, N.
AU - Beauchemin, S.
AU - Bauer, M.
PY - 2021
SP - 450
EP - 457
DO - 10.5220/0010241004500457
PB - SciTePress

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