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Enhanced YOLOv8n-Based Three-Module Lightweight Helmet Detection System.

Xinyu Zuo1, Yiqing Dai2, Chao Yu2

  • 1School of Architecture and Design, China University of Mining and Technology, Xuzhou 221000, China.

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Summary
This summary is machine-generated.

This study presents an improved YOLOv8n model for efficient construction safety helmet detection. The enhanced algorithm offers higher accuracy and faster processing, making safety compliance more accessible.

Keywords:
YOLOv8nconstruction safetyhelmet detectionlightweight

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Area of Science:

  • Construction Safety
  • Computer Vision
  • Artificial Intelligence

Background:

  • Current safety helmet detection systems face challenges like low accuracy and high deployment costs.
  • Complex construction site backgrounds often hinder the performance of existing detection technologies.

Purpose of the Study:

  • To develop an efficient and accurate safety helmet detection method for construction sites.
  • To overcome the limitations of existing helmet detection technologies, including accuracy and cost.

Main Methods:

  • An improved YOLOv8n model incorporating C2f-SCConv architecture, Partial Convolutional Detector (PCD), and Coordinate Attention (CA).
  • Coordinate Attention (CA) enhances detection, reduces redundancy, and improves feature localization.
  • Partial Convolutional Detector (PCD) refines features and reduces computational costs.
  • C2f-SCConv module replaces the bottleneck C2f module for improved feature representation.

Main Results:

  • The upgraded YOLOv8n model achieved an average accuracy of 94.4%.
  • Significant improvements were observed: a 2.21 MB reduction in model size, a 12.6% increase in frame rate, and a 49.9% decrease in FLOPs.
  • The new method demonstrates superior efficiency, speed, and cost-effectiveness compared to conventional algorithms.

Conclusions:

  • The proposed method offers a more efficient, faster, and cost-effective solution for on-site safety helmet detection.
  • This advancement contributes to maintaining safer working environments in urban construction projects.
  • The enhanced YOLOv8n model provides a practical and reliable tool for improving construction safety compliance.