Evaluation of AIoT performance in Cloud and Edge computational models for mask detection

Main Article Content

Felipe Quiñonez-Cuenca https://orcid.org/0000-0001-7221-4700
Cristian Maza-Merchán https://orcid.org/0000-0002-2078-8267
Nilvar Cuenca-Maldonado https://orcid.org/0000-0002-2611-1310
Manuel Quiñones-Cuenca https://orcid.org/0000-0002-2932-1524
Rommel Torres https://orcid.org/0000-0003-2313-0118
Francisco Sandoval https://orcid.org/0000-0001-5167-0256
Patricia Ludeña-González https://orcid.org/0000-0002-8909-4837

Abstract

COVID-19 has caused serious health damage, infecting millions of people and unfortunately causing the death of several ones around the world. The vaccination programs of each government have influenced in declining those rates. Nevertheless, new coronavirus mutations have emerged in different countries, which are highly contagious, causing concern with vaccination effectiveness. So far, wearing facemasks in public continues being the most effective protocol to avoid and prevent COVID-19 spread. In this context, there is a demand of automatic facemask detection services to remind people the importance of wearing them appropriately. In this work, a performance evaluation of an AIoT system to detect correct, inappropriate, and non- facemask wearing, based on two computational models: Cloud and Edge, was conducted. Having as objective to determine which model better suites a real environment (indoor and \emph{outdoor}), based on: reliability of the detector algorithm, use of computational resources, and response time. Experimental results show that Edge-implementation got better performance in comparison to Cloud-implementation.
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