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Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4.

Jimin Yu1,2, Wei Zhang1,2

  • 1College of Automation, Chongqing University of Post and Telecommunications, Chongqing 400065, China.

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|June 2, 2021
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Summary
This summary is machine-generated.

This study introduces an improved YOLO-v4 algorithm for accurate and real-time face mask detection. The enhanced model achieves high performance in complex environments, improving public safety.

Keywords:
CSPDarknNet53PANetYOLO-v4adaptive image scalingface mask recognition

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Existing face mask detection algorithms struggle with accuracy and real-time performance in complex environments.
  • Robustness issues hinder the practical application of current detection methods.

Purpose of the Study:

  • To develop an improved face mask recognition and standard wear detection algorithm.
  • To enhance accuracy, real-time performance, and robustness in complex environments.

Main Methods:

  • Utilized an improved CSPDarkNet53 for feature extraction, reducing computational cost and enhancing model learning.
  • Implemented an adaptive image scaling algorithm to minimize computation and redundancy.
  • Introduced an improved PANet structure to enrich feature layers with semantic information.
  • Created a dedicated face mask detection dataset for training and evaluation.

Main Results:

  • Achieved a mean Average Precision (mAP) of 98.3% for face mask recognition.
  • Attained a high frame rate of 54.57 FPS, indicating excellent real-time performance.
  • Demonstrated superior accuracy and performance compared to existing algorithms.

Conclusions:

  • The proposed improved YOLO-v4 algorithm effectively addresses limitations in current face mask detection systems.
  • The algorithm offers a highly accurate and efficient solution for real-time standard face mask wear detection.