Urban Development Policy Making

Urban Development Policy Making

Performance Evaluation of the YOLOv8 Model in Traffic Sign Detection for Intelligent Transportation System

Document Type : Original Article

Authors
Sana Nazarinezhad 1
Elham Farahani 2
1 M.Sc. in Information Technology Engineering, Department of Information Technology, Faculty of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
2 Assistant Professor, Faculty of Computer Engineering, Iranian eUniversity, Tehran, Iran
10.22034/judpm.2025.514770.1029
Abstract
The increasing urbanization and vehicle growth strain traditional transportation systems, necessitating data-driven intelligent solutions. This article explores the application of artificial intelligence, specifically computer vision, in urban traffic management. We employed YOLOv8, the latest YOLO model, for traffic sign detection. Its improved architecture offers enhanced speed and accuracy for real-time object detection. Trained and evaluated on the Self-Driving Cars dataset, YOLOv8 demonstrated acceptable performance in traffic sign detection, suggesting its potential for intelligent urban traffic monitoring and improved road safety. However, the study identifies challenges in detecting certain traffic sign classes and proposes future directions, including multimodal models, increased training data diversity, and lightweight hardware implementation.
Keywords
Artificial Intelligence
Computer Vision
Deep Learning
Smart Cities
Traffic Management

Subjects

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Urban Development Policy Making
Volume 2, Issue 3
Autumn 2025
Pages 277-296

  • Receive Date 18 March 2025
  • Revise Date 18 April 2025
  • Accept Date 15 May 2025
  • Publish Date 01 June 2025