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Multistage Framework for Automatic Face Mask Detection Using Deep Learning.

Sowmya K N1, Rekha P M1, Trishala Kumari2

  • 1Department of Information Science and Engineering, JSS Academy of Technical Education, Bangalore, India.

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Summary
This summary is machine-generated.

A new two-stage AI model efficiently detects face mask usage and appropriateness in real-time video streams. This technology aids in monitoring public health compliance and mitigating COVID-19 transmission.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Health Technology

Background:

  • The COVID-19 pandemic necessitates continuous monitoring of public health measures.
  • Effective mitigation of virus transmission requires adherence to face mask policies.
  • Real-time surveillance of mask-wearing behavior is crucial for public safety.

Purpose of the Study:

  • To develop a low-cost, efficient two-stage model for detecting face mask usage and appropriateness.
  • To enable real-time monitoring of individuals in public spaces and workplaces.
  • To support efforts in controlling the spread of infectious diseases like COVID-19.

Main Methods:

  • A two-stage deep learning model was designed for face mask detection.
  • The model classifies faces with and without masks, and assesses mask appropriateness.
  • Convolutional Neural Network (CNN) architectures including MobileNetV2, ResNet50V2, and InceptionV3 were evaluated using transfer learning.
  • MobileNetV2 was selected as the backbone for its high accuracy.

Main Results:

  • The proposed model demonstrated high efficacy in detecting face masks across various inputs (images, videos, live streams).
  • The model achieved an accuracy of 98.44% using MobileNetV2 as the backbone.
  • Performance was validated on conventional and custom datasets, including diverse mask types.

Conclusions:

  • The developed AI model offers a practical solution for monitoring face mask compliance.
  • This technology can be instrumental in public health surveillance and disease control strategies.
  • The lightweight and efficient design makes it suitable for widespread deployment in various settings.