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Masking and Demasking Agents

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Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework.

Xiangjie Kong¹, Kailai Wang², Shupeng Wang³

¹College of Computer Science and TechnologyZhejiang University of Technology Hangzhou 310023 China.

IEEE Internet of Things Journal

|July 5, 2022

Summary

This summary is machine-generated.

This study introduces an edge computing-based mask (ECMask) identification framework for real-time mask detection on low-power devices. The ECMask system effectively ensures public health precautions by accurately identifying mask usage in real-world scenarios.

Keywords:

Coronavirus disease 2019 (COVID-19)Internet of Things (IoT)deep learning edge computing mask identification public health prevention

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Area of Science:

Computer Science
Public Health
Artificial Intelligence

Background:

The COVID-19 pandemic highlighted the need for effective public health interventions, such as mask-wearing.
The Internet of Medical Things (IoMT) offers opportunities for enhanced data collection but also presents challenges for real-time public health monitoring.
Automatic, real-time mask detection is crucial for epidemic prevention and control.

Purpose of the Study:

To develop an efficient, real-time mask detection framework for public health applications.
To ensure timely and accurate identification of mask compliance on low-power edge devices.
To support COVID-19 prevention strategies through technological intervention.

Main Methods:

An edge computing-based mask (ECMask) identification framework was proposed.
The ECMask framework comprises three stages: video restoration, face detection, and mask identification.
Models were trained and evaluated using a dedicated bus drive monitoring dataset and a public dataset.

Main Results:

The ECMask framework demonstrated good performance in terms of detection accuracy and execution time efficiency.
Extensive experiments validated the framework's effectiveness on real video data.
The system is suitable for real-time video analysis on resource-constrained devices.

Conclusions:

The developed ECMask framework provides a valuable tool for real-time mask detection in public spaces.
This edge computing approach offers a practical solution for enhancing public health precautions during epidemics.
The system has significant potential for application in COVID-19 prevention and similar public health crises.