Traffic Sign Orientation Estimation from Images Using Deep Learning

Raluca-Diana Chiş, Mihai-Adrian Loghin, Cristina Mierlă, Horea Bogdan Mureşan, Octav-Cristian Florescu

2025

Abstract

This study presents our findings on estimating the horizontal rotation angle (yaw) of traffic signs from 2D images using deep learning techniques. The aim is to introduce novel approaches for accurately estimating a traffic sign’s orientation, with applications in automatic map generation. The primary goal is to associate a traffic sign with a road correctly. The main challenge consists of both attempting to estimate the left/right orientation of a sign from 2D images and accurately estimating the rotation of the sign in degrees. Our approach involves the usage of a classifier for determining the orientation of a traffic sign in relation to the observer. Furthermore, we tried to transfer the weights obtained from classification to regression models and study the impact on performance. Our best results are obtaining an L1 loss as low as 10.34◦for yaw estimation and an accuracy equal to 62% for orientation class assessment. The image data was obtained from Grab’s Kartaview platform and was split into training/validation/testing while accounting for traffic sign class and shape balancing.

Paper Citation

in Harvard Style

Chiş R., Loghin M., Mierlă C., Mureşan H. and Florescu O. (2025). Traffic Sign Orientation Estimation from Images Using Deep Learning. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 233-240. DOI: 10.5220/0013115900003890

in Bibtex Style

@conference{icaart25,
author={Raluca-Diana Chiş and Mihai-Adrian Loghin and Cristina Mierlă and Horea Mureşan and Octav-Cristian Florescu},
title={Traffic Sign Orientation Estimation from Images Using Deep Learning},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={233-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013115900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Traffic Sign Orientation Estimation from Images Using Deep Learning
SN - 978-989-758-737-5
AU - Chiş R.
AU - Loghin M.
AU - Mierlă C.
AU - Mureşan H.
AU - Florescu O.
PY - 2025
SP - 233
EP - 240
DO - 10.5220/0013115900003890
PB - SciTePress