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This study introduces an advanced AI system for accurate, real-time facemask detection and tracking using video. The system achieves high performance, aiding public health monitoring.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Health Technology

Background:

  • Effective mask-wearing is crucial for public health, yet automated detection and tracking systems are underdeveloped.
  • Existing image processing research lacks robust solutions for real-time facemask status identification.

Purpose of the Study:

  • To develop a high-performance, two-stage facemask detector and tracker using a monocular camera and deep learning.
  • To automate the detection and tracking of facemask usage in video sequences.

Main Methods:

  • Utilized a novel dataset of 18,000 images with over 30,000 annotations for 'face masked', 'incorrectly masked', and 'no masked' classes.
  • Employed the Scaled-You Only Look Once (Scaled-YOLOv4) model for detection (YOLOv4-P6-FaceMask) and Simple Online and Real-time Tracking with a deep association metric (DeepSORT) for tracking.
  • Implemented DeepSORT for efficient face tracking and database creation of individuals without masks.

Main Results:

  • The YOLOv4-P6-FaceMask detector achieved 93% mean average precision and 92% mean average recall.
  • The system demonstrated real-time processing speeds of 35 frames per second on a single Tesla-T4 GPU.
  • Performance was validated against state-of-the-art facemask detection and tracking models.

Conclusions:

  • The proposed deep learning framework offers a highly accurate and efficient solution for automated facemask detection and tracking.
  • This technology can significantly enhance public health surveillance and compliance monitoring in real-world scenarios.