An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5

Inderpreet Singh Walia,Deepika Kumar,Kaushal Sharma,Jude D Hemanth,Daniela Elena Popescu

doi:10.3390/electronics10232996

Inderpreet Singh Walia, Deepika Kumar + Show 3 more

Open Access

https://doi.org/10.3390/electronics10232996

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Abstract

SARS-CoV-19 is one of the deadliest pandemics the world has witnessed, taking around 5,049,374 lives till now across worldwide and 459,873 in India. To limit its spread numerous countries have issued many safety measures. Though vaccines are available now, still face mask detection and maintain social distance are the key aspects to prevent from this pandemic. Therefore, authors have proposed a real-time surveillance system that would take the input video feed and check whether the people detected in the video are wearing a mask, this research further monitors the humans for social distancing norms. The proposed methodology involves taking input from a CCTV feed and detecting humans in the frame, using YOLOv5. These detected faces are then processed using Stacked ResNet-50 for classification whether the person is wearing a mask or not, meanwhile, DBSCAN has been used to detect proximities within the persons detected.

Highlights

COVID-19 is an infectious virus that caused by extreme acute respiratory syndrome (SARS-CoV-2)
This work aims to present face mask detection and deep learning social distance surveillance consecutively to detect that the social distancing norms are followed in the public area to minimize the rise of COVID-19 cases
The proposed research has been accomplished in two parts, first, classification of face mask using Stacked ResNet-50 and monitoring social distancing using DBSCAN clustering

Summary

Introduction

COVID-19 (coronavirus disease) is an infectious virus that caused by extreme acute respiratory syndrome (SARS-CoV-2). SARS-CoV-2 is a coronavirus that triggers a respiratory tract infection It was first discovered in December 2019 in Wuhan, Hubei, China, and has since caused an ongoing pandemic with multiple deaths all over the world. This work aims to present face mask detection and deep learning social distance surveillance consecutively to detect that the social distancing norms are followed in the public area to minimize the rise of COVID-19 cases. This might help overcome hardware requirements, installation costs, human resources.

Related Work

Materials and Methods

Face mask Classifier using Stacked ResNet-50

Face Mask Classifier Using Stacked ResNet-50

Findings

Conclusions and Future Scope