Creating an Automatic Road Sign Inventory System using a Fully Deep Learning-based Approach

Gabriele Galatolo, Matteo Papi, Andrea Spinelli, Guglielmo Giomi, Andrea Zedda, Marco Calderisi

2022

Abstract

Some road sections are a veritable forest of road signs: just think how many indications you can come across on an urban or extra-urban route, near a construction site or a road diversion. The automatic recognition of vertical traffic signs is an extremely useful task in the automotive industry for many practical applications, such as supporting the driver while driving with an in-car advisory system or the creation of a register of signals for a particular road section to speed up maintenance and replacement of installations. Recent developments in deep learning have brought huge progress in the image processing area, which triggered successful applications like traffic sign recognition (TSR). The TSR is a specific image processing task in which real traffic scenes (images or frames from videos taken from vehicle cameras in uncontrolled lighting and occlusion conditions) are processed in order to detect and recognize traffic signs within it. Traffic Sign Recognition is a very recent technology facilitated by the Vienna Convention on Road Signs and Signals of 1968: during that international meeting, it was decided to standardize traffic signs so that they could be recognised more easily abroad. Finally, this work summarizes our proposal of a practical pipeline for the development of an automatic traffic sign recognition software.

Paper Citation

in Harvard Style

Galatolo G., Papi M., Spinelli A., Giomi G., Zedda A. and Calderisi M. (2022). Creating an Automatic Road Sign Inventory System using a Fully Deep Learning-based Approach. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 102-109. DOI: 10.5220/0011266100003277

in Bibtex Style

@conference{delta22,
author={Gabriele Galatolo and Matteo Papi and Andrea Spinelli and Guglielmo Giomi and Andrea Zedda and Marco Calderisi},
title={Creating an Automatic Road Sign Inventory System using a Fully Deep Learning-based Approach},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={102-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011266100003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Creating an Automatic Road Sign Inventory System using a Fully Deep Learning-based Approach
SN - 978-989-758-584-5
AU - Galatolo G.
AU - Papi M.
AU - Spinelli A.
AU - Giomi G.
AU - Zedda A.
AU - Calderisi M.
PY - 2022
SP - 102
EP - 109
DO - 10.5220/0011266100003277