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

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 dr...

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Bibliographic Details
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
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Summary: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.
ISSN:2412-3811
2412-3811
DOI:10.3390/infrastructures8020020