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

• Published in: Infrastructures (Basel) 2023-01, Vol.8 (2), p.20
• Main Authors: Kim, Chang-il, Park, Jinuk, Park, Yongju, Jung, Woojin, Lim, Yong-seok
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Autonomous vehicles; Deep learning; Driving; Identification and classification; Machine learning; Methods; object detection; Object recognition; Performance evaluation; real-time application; Real-time control; Real-time systems; Road conditions; Roads & highways; Safety and security measures; Signs; Tracking; Traffic control; traffic sign recognition; Traffic signs; Traffic signs and signals; Urban ecology; Urban environments; urban road scene
• ISSN: 2412-3811; 2412-3811
• DOI: 10.3390/infrastructures8020020

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.