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A Face Detection and Standardized Mask-Wearing Recognition Algorithm.

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Summary
This summary is machine-generated.

This study introduces an advanced YOLOv4-based system for real-time mask detection, improving accuracy in public spaces. The new approach enhances object identification and positioning for effective COVID-19 safety monitoring.

Keywords:
bounding box regressionconfidencefeature pyramidalwearing mask detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Health Technology

Background:

  • The COVID-19 pandemic necessitates effective public health measures, including mask-wearing.
  • Accurate and rapid detection of mask compliance in public spaces is crucial for controlling virus transmission.
  • Existing detection algorithms require improvements in accuracy and real-time performance.

Purpose of the Study:

  • To develop a high-accuracy, real-time algorithm for detecting faces and regulating mask usage.
  • To enhance object detection and bounding box regression for improved performance, especially with small objects.
  • To introduce a new dataset for training and evaluating face and mask recognition models.

Main Methods:

  • A single-stage object detection approach based on YOLOv4 was proposed.
  • A novel attention-based feature pyramidal network was developed to minimize information loss during convolutional neural network processing.
  • A new bounding box regression function, Norm CIoU (NCIoU), was introduced, improving positioning accuracy, particularly for small objects.

Main Results:

  • The proposed method achieved a mean Average Precision (mAP) of 69.70% at IoU threshold .5:.95 and 73.80% at IoU threshold .75.
  • The NCIoU function demonstrated improved accuracy in object detection and bounding box regression.
  • The developed face and mask recognition (RFM) dataset comprises 12,133 images across three categories: face, standardized mask, and non-standardized mask.

Conclusions:

  • The proposed YOLOv4-based approach with attention mechanisms and NCIoU significantly enhances real-time mask detection accuracy.
  • The method effectively addresses challenges in detecting small objects and mitigating algorithmic bias.
  • The RFM dataset provides a valuable resource for advancing research in face and mask recognition for public health applications.