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Research on helmet wearing detection method based on deep learning.

Lihong Wei1, Panpan Liu2, Haihui Ren3

  • 1School of Artificial Intelligence and Big Data, Hulunbeier University, Inner Mongolia, 021008, Hailar, China.

Scientific Reports
|March 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved deep learning model for real-time safety helmet detection in construction. The enhanced BiFEL-YOLOv5s model improves accuracy and recall, crucial for worker safety.

Keywords:
Attention mechanismDeep learningHelmet-wearing detectionObject detectionYOLOv5

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Occupational Safety

Background:

  • Construction industry growth presents significant safety challenges.
  • Safety helmets are critical for reducing worker casualties.
  • Real-time detection of safety helmet usage is needed.

Purpose of the Study:

  • To develop a deep learning model for real-time safety helmet detection.
  • To improve upon the YOLOv5s network for better performance on small and occluded objects.
  • To enhance detection speed and accuracy for practical construction site application.

Main Methods:

  • Utilized YOLOv5s network as a base for development.
  • Incorporated multiple attention mechanisms to enhance feature extraction.
  • Improved the feature pyramid network to BiFPN (Bidirectional Feature Pyramid Network).
  • Replaced Non-Maximum Suppression (NMS) with Soft-NMS for improved detection.
  • Introduced Focal-EIoU Loss to optimize model convergence and speed.
  • Proposed the BiFEL-YOLOv5s network model.

Main Results:

  • The BiFEL-YOLOv5s model achieved a 0.9% increase in average precision.
  • Recall rate was improved by 2.8%.
  • Detection speed was maintained with minimal decrease.
  • The model demonstrated suitability for real-time safety helmet detection.

Conclusions:

  • The proposed BiFEL-YOLOv5s model effectively addresses real-time safety helmet detection requirements.
  • Improvements in attention mechanisms, BiFPN, Soft-NMS, and Focal-EIoU Loss contribute to enhanced performance.
  • The model is well-suited for various construction work scenarios, improving occupational safety.