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DTLMV2-A real-time deep transfer learning mask classifier for overcrowded spaces.

Meenu Gupta1,2, Gopal Chaudhary3, Dhruvi Bansal3

  • 1Department of Computer Science and Engineering, Chandigarh University, Punjab, India.

Applied Soft Computing
|July 26, 2022
PubMed
Summary

A new Deep Transfer Learning MobileNetV2 (DTLMV2) model accurately detects face mask usage. This computer vision system offers a low-cost solution for public spaces, aiding in COVID-19 prevention efforts.

Keywords:
CNNCOVID19Computer visionDeep learningMask classifierMobileNetV2Object detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Public Health

Background:

  • The COVID-19 pandemic necessitates effective strategies to curb viral transmission.
  • Widespread mask-wearing is a key preventative measure, yet compliance varies.
  • Monitoring mask usage in public spaces is crucial for public health surveillance.

Purpose of the Study:

  • To develop and implement a reliable face mask identification model.
  • To create a low-cost, efficient system for detecting mask usage in real-time.
  • To leverage deep learning for public health monitoring during the pandemic.

Main Methods:

  • Proposed DTLMV2 (Deep Transfer Learning MobileNetV2) model for classification.
  • Utilized MobileNetV2, a lightweight Convolutional Neural Network (CNN).
  • Integrated computer vision techniques for real-time image and video analysis.

Main Results:

  • Achieved 97.01% accuracy on validation data.
  • Attained 98% accuracy on training data.
  • Demonstrated 97.45% accuracy on testing data.

Conclusions:

  • The DTLMV2 model provides a highly accurate method for face mask detection.
  • The system is suitable for deployment in open public spaces and surveillance.
  • This technology can support public health initiatives by monitoring mask compliance.