Design of a face mask detection system to control the entry of people to a medical center

The arrival of COVID-19 completely changed people's lives, at the end of December 2019 a new virus appeared that forced people to maintain social distancing and wear a mask in a mandatory way, because the virus caused by SARS-COV-2 is highly contagious and can be easily transmitted between people being announced as a pandemic by the World Health Organization. In the month of May, the virus infected more than 153 million people saturating the health system worldwide, therefore, it was established as a preventive measure to maintain social distancing in public places and use the mask in a mandatory way to help control COVID-19, as it reduces the amount of saliva emissions and respiratory microdroplets of individuals carrying the virus. In view of this problem, this article designed a face mask detection system to control the entry of people into a medical center, so that the system analyzes several people who want to enter the place and shows if they use the mask correctly through a monitor, requiring to respect this important biosecurity measure, in addition to being able to be implemented anywhere you want to control the entry of people with a mask. By designing the system, it can be said that the system works in the best way and would be helping to prevent several people from continuing to catch COVID-19. © 2022 IEEE.

Real-time monitoring of social distancing with person marking and tracking system using YOLO V3 model

The global economy has been affected enormously due to the spread of coronavirus (COVID-19). Even though, there is the availability of vaccines, social distancing in public places is one of the viable solutions to reduce the spreading of COVID-19 suggested by the World Health Organization (WHO) for fighting against the pandemic. This paper presents a YOLO v3 object detection model to automate the monitoring of social distancing among persons through a CCTV surveillance camera. Furthermore, this research work used to detect and track the person, measure the inter-person distance in the crowd under a challenging environment which includes partial visibility, lighting variations, and person occlusion. Moreover, the YOLO V3 model experiments with Darknet53 and ShuffleNetV2 backbone architecture. Compared with Darknet53 architecture, ShuffleNetV2 achieves better detection accuracy tested on Custom Video Footage Dataset (CVFD), Oxford Town Centre Dataset (OTCD), and Custom Personal Image Dataset (CPID) datasets.

Design of a Distance Detection System for Patients in a Health Center

COVID-19 was declared by the World Health Organization (WHO) as a pandemic on March 11th, 2020, because it is a highly contagious viral infection. It infected more than 153 million people until the beginning of May 2021 and caused more than 3.2 million deaths worldwide. In Peru, the first case was reported on March 6th, 2020, days later the entire country was quarantined to prevent the spread of COVID-19 and not further complicate the health system. With this, it affected economically many people who did not have a full-time job;therefore, the government reactivated some activities if they comply with biosafety measures, among them is social distancing, being important to keep it at least two meters from other people whenever possible, but unfortunately people fail to comply with this measure. In view of this problem, this article will carry out the design of a distance detection system for patients in a health center, in such a way that the system performs the most exact measurement of the distance between people, and it can be implemented in any place where there is an agglomeration of people to put order within the place. Through, the design of the system, will work in the most appropriate way, in such a way that it will help to prevent the spread of COVID-19 that currently continues to affect many countries. Copyright © 2022 Korean Society of Gastrointestinal Endoscopy.