Road Lane Detection and Classification in Urban and Suburban Areas based on CNNs

Nima Khairdoost, Steven Beauchemin, Michael Bauer

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.

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Paper Citation


in Harvard Style

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 - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 450-457. DOI: 10.5220/0010241004500457


in Bibtex Style

@conference{visapp21,
author={Nima Khairdoost and Steven Beauchemin and Michael 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 - Volume 4: VISAPP,},
year={2021},
pages={450-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010241004500457},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Road Lane Detection and Classification in Urban and Suburban Areas based on CNNs
SN - 978-989-758-488-6
AU - Khairdoost N.
AU - Beauchemin S.
AU - Bauer M.
PY - 2021
SP - 450
EP - 457
DO - 10.5220/0010241004500457