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Updated: Jun 28, 2025

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
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Helmet wearing detection algorithm based on improved YOLOv5.

Yiping Liu1, Benchi Jiang2, Huan He3

  • 1School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, People's Republic of China.

Scientific Reports
|April 16, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances helmet detection in industrial settings using an improved YOLOv5 algorithm. The new model offers higher accuracy and faster real-time detection, crucial for worker safety.

Keywords:
Attention mechanismDeep learningNetwork structureTarget detectionYOLOv5

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Safety

Background:

  • Accurate helmet detection is vital for worker safety in industrial environments.
  • Existing methods struggle with varying lighting, viewing angles, and occlusions, limiting detection precision.

Purpose of the Study:

  • To develop a real-time helmet detection system with improved accuracy and efficiency.
  • To address the limitations of current target detection algorithms in challenging industrial conditions.

Main Methods:

  • Improved the YOLOv5 algorithm by integrating the FasterNet lightweight network structure.
  • Implemented the Wise-IoU loss function with a dynamic focusing mechanism.
  • Introduced the CBAM attention mechanism to enhance global context and small target detection.

Main Results:

  • Reduced model parameters by 12.68% and computational load by 10.8%.
  • Increased mean Average Precision (mAP) from 88.3% to 92.3%.
  • Decreased inference time by 81.5%, enabling effective real-time detection.

Conclusions:

  • The enhanced YOLOv5 model significantly improves helmet detection performance.
  • The modifications lead to a more efficient and accurate system for real-time industrial safety monitoring.