0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Deep Learning-Based Real-Time Traffic Sign Recognition System for Urban Environments

Auteur(s): (Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea)
ORCID (Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea)
(Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea)
(Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea)
(Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea)
Médium: article de revue
Langue(s): anglais
Publié dans: Infrastructures, , n. 2, v. 8
Page(s): 20
DOI: 10.3390/infrastructures8020020
Abstrait:

A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition.

Copyright: © 2023 the Authors. Licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10722751
  • Publié(e) le:
    22.04.2023
  • Modifié(e) le:
    10.05.2023
 
Structurae coopère avec
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine