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Authors: Maxime Tual ; Valérie Muzet ; Philippe Foucher ; Christophe Heinkelé and Pierre Charbonnier

Affiliation: Research Team ENDSUM, Cerema, 11 rue Jean Mentelin, 67035 Strasbourg, France

Keyword(s): Road Marking, Deep Learning, Regression, Evaluation, Laser Data, LCMS Sensor.

Abstract: Road markings are essential guidance elements for both drivers and driver assistance systems: their maintenance requires regularly scheduled performance surveys. In this paper, we introduce a deep learning based method to estimate two indicators of the quality of road markings (the percentage of remaining marking and the contrast) directly from their appearance, using reflectance data acquired by a mobile laser imaging system used for inspections. To do this, we enhance the EfficientDet architecture by adding an output sub-network to predict the indicators. It is not possible to physically establish large-scale reference measurements for training and testing our model, but this can be done indirectly by semi-supervised image annotation, a strategy validated by our experiments. Our results show that it is advisable to train the model end-to-end without optimizing its detection performance. They also enlighten the very good accuracy of the indicators predicted by the model.

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Paper citation in several formats:
Tual, M.; Muzet, V.; Foucher, P.; Heinkelé, C. and Charbonnier, P. (2024). Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging. In Proceedings of the 4th International Conference on Image Processing and Vision Engineering - IMPROVE; ISBN 978-989-758-693-4; ISSN 2795-4943, SciTePress, pages 23-31. DOI: 10.5220/0012595600003720

@conference{improve24,
author={Maxime Tual. and Valérie Muzet. and Philippe Foucher. and Christophe Heinkelé. and Pierre Charbonnier.},
title={Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging},
booktitle={Proceedings of the 4th International Conference on Image Processing and Vision Engineering - IMPROVE},
year={2024},
pages={23-31},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012595600003720},
isbn={978-989-758-693-4},
issn={2795-4943},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Image Processing and Vision Engineering - IMPROVE
TI - Using Deep Learning for the Dynamic Evaluation of Road Marking Features from Laser Imaging
SN - 978-989-758-693-4
IS - 2795-4943
AU - Tual, M.
AU - Muzet, V.
AU - Foucher, P.
AU - Heinkelé, C.
AU - Charbonnier, P.
PY - 2024
SP - 23
EP - 31
DO - 10.5220/0012595600003720
PB - SciTePress

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