A Deep Learning-based Approach for Real-time Facemask Detection

TL;DR

This paper presents a deep learning approach using MobileNetV2 for real-time facemask detection, achieving high accuracy and efficiency suitable for deployment in public spaces during the COVID-19 pandemic.

Contribution

The study introduces a mobile-friendly deep learning model for real-time facemask detection, outperforming several state-of-the-art models in accuracy and training efficiency.

Findings

Achieved 99% accuracy in facemask detection

MobileNetV2 outperforms ResNet50, DenseNet, and VGG16 in speed and accuracy

Effective deployment at edge computing for real-time monitoring

Abstract

The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask becomes one of the effective protection solutions adopted by many governments. Manual real-time monitoring of facemask wearing for a large group of people is becoming a difficult task. The goal of this paper is to use deep learning (DL), which has shown excellent results in many real-life applications, to ensure efficient real-time facemask detection. The proposed approach is based on two steps. An off-line step aiming to create a DL model that is able to detect and locate facemasks and whether they are appropriately worn. An online step that deploys the DL model at edge computing in order to detect masks in real-time. In this study, we propose to use MobileNetV2 to detect facemask in real-time. Several experiments are conducted and show good performances of the proposed approach (99% for training and testing accuracy). In addition, several comparisons with many state-of-the-art models namely ResNet50, DenseNet, and VGG16 show good performance of the MobileNetV2 in terms of training time and accuracy.