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Updated: Jul 12, 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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Research on improved algorithm for helmet detection based on YOLOv5.

Chun Shan1,2, HongMing Liu3, Yu Yu3

  • 1Guangdong Polytechnic Normal University, Guangzhou, China. shanchun@gpnu.edu.cn.

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|October 23, 2023
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Summary
This summary is machine-generated.

This study enhances safety helmet detection using an improved YOLOv5 model, achieving 95.9% accuracy. The advanced method reduces missed detections in complex industrial environments, improving worker safety.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Safety

Background:

  • Smart industrial parks require robust safety helmet detection.
  • Existing methods struggle with small, dense targets, leading to false alarms and missed detections.

Purpose of the Study:

  • To enhance the YOLOv5 target detection algorithm for real-time safety helmet detection.
  • To improve accuracy and reduce errors in complex industrial settings.

Main Methods:

  • Incorporated ECA channel attention mechanism into YOLOv5 backbone for efficient feature extraction.
  • Utilized a weighted bi-directional feature pyramid network (BiFPN) for effective feature fusion.
  • Introduced a decoupling head to improve detection performance and convergence.

Main Results:

  • The enhanced YOLOv5 model achieved 95.9% average accuracy on a custom helmet dataset.
  • Demonstrated a 3.0 percentage point accuracy increase over the original YOLOv5.
  • Showcased significant improvements in detecting safety helmet compliance in complex scenarios.

Conclusions:

  • The modified YOLOv5 model offers superior performance for safety helmet detection.
  • This approach enhances accuracy and robustness, crucial for industrial safety applications.
  • The study provides a more reliable solution for real-time monitoring in smart industrial parks.