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Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4.

Yongjie Wang1, Miaoyuan Bai1, Mingzhi Wang1

  • 1College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.

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This study introduces an improved YOLOv4 model for robust traffic sign detection in complex environments. The enhanced method significantly boosts accuracy for detecting traffic signs, crucial for autonomous driving systems.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Traffic sign detection is critical for autonomous driving safety.
  • Complex environments pose significant challenges to existing detection methods.
  • Multiscale traffic sign recognition requires robust feature extraction.

Purpose of the Study:

  • To develop an advanced traffic sign detection and recognition method for complex environments.
  • To improve the accuracy and reliability of traffic sign detection in autonomous driving.
  • To enhance the model's ability to handle multiscale traffic signs.

Main Methods:

  • An improved You Only Look Once (YOLO) v4 architecture was utilized.
  • Image preprocessing for denoising and classification in complex scenes was implemented.
  • An enhanced feature pyramid structure with adaptive fusion and multiscale transfer was designed.
  • EIOU LOSS and Cluster-NMS were incorporated to refine performance.

Main Results:

  • The proposed method achieved a mean Average Precision (mAP) of 81.78% on a combined dataset.
  • Experimental results demonstrated superior performance compared to existing traffic sign detection methods.
  • The enhanced feature pyramid improved information transfer and representation ability.

Conclusions:

  • The improved YOLOv4 method offers a highly effective solution for traffic sign detection in challenging conditions.
  • The adaptive feature fusion and multiscale transfer mechanisms are key to the enhanced performance.
  • This approach significantly advances the capabilities of autonomous driving systems.