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Efficient traffic sign recognition using YOLO for intelligent transport systems.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Traffic sign recognition (TSR) is crucial for autonomous driving safety.
  • Existing systems face challenges with small targets, environmental variations, and real-time processing.

Purpose of the Study:

  • To develop an optimized YOLOv5-based framework for robust and efficient TSR.
  • To enhance detection of small traffic signs and improve performance under various environmental conditions.

Main Methods:

  • Implemented k-means++ for anchor box optimization to improve small-target detection.
  • Conducted comparative analysis of YOLOv5 variants (s/m/x) for precision-speed trade-offs.
  • Performed systematic hyperparameter tuning for enhanced robustness.

Main Results:

  • Achieved 98.1% mean average precision (mAP) and 99.3% precision on the CCTSDB dataset.
  • Outperformed Faster-RCNN and SSD by 5-8% in mAP while maintaining 45 FPS.
  • YOLOv5s variant demonstrated optimal balance with 99.3% mAP@0.5 and 32 ms inference time.
  • Showcased robust performance in challenging scenarios like fog and backlighting.

Conclusions:

  • The optimized YOLOv5 framework significantly enhances TSR accuracy and reliability.
  • The system is well-suited for real-time applications in intelligent transportation systems.
  • The proposed optimizations offer a flexible and high-performance solution for autonomous driving.