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Lightweight Helmet Detection Algorithm Using an Improved YOLOv4.

Junhua Chen1,2, Sihao Deng2, Ping Wang2

  • 1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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|February 11, 2023
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Summary
This summary is machine-generated.

This study introduces an improved YOLOv4 algorithm for real-time safety helmet detection, enhancing worker safety in construction. The new model achieves high accuracy and speed while significantly reducing size.

Keywords:
PP-LCNetSIoUYOLOv4attention mechanismfeature fusionhelmet detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Occupational Safety

Background:

  • Safety helmet compliance is critical for worker protection in industrial and construction environments.
  • Real-time detection systems are essential for enforcing safety helmet usage.
  • Existing detection methods may lack efficiency or accuracy.

Purpose of the Study:

  • To develop an improved YOLOv4 algorithm for efficient and accurate real-time safety helmet detection.
  • To enhance the performance of object detection models for safety applications.
  • To reduce model size and increase detection speed.

Main Methods:

  • An improved YOLOv4 algorithm was proposed, utilizing PP-LCNet as a lightweight backbone.
  • Deepwise separable convolution was employed to minimize model parameters.
  • Coordinate attention mechanism and an improved feature fusion structure were integrated.
  • SIoU loss function was adopted to enhance detection precision.

Main Results:

  • The improved YOLOv4 algorithm achieved 92.98% accuracy with a model size of 41.88 MB and a detection speed of 43.23 images/s.
  • Compared to the original YOLOv4, accuracy increased by 0.52%, model size decreased by 83%, and detection speed improved by 88%.
  • The proposed method demonstrated superior precision and speed compared to existing approaches.

Conclusions:

  • The improved YOLOv4 algorithm offers a highly accurate, efficient, and lightweight solution for real-time safety helmet detection.
  • This technology can significantly contribute to improving safety compliance in industrial and construction settings.
  • The integration of lightweight networks, attention mechanisms, and advanced loss functions proves effective for object detection tasks.