Deep Learning-based Face Mask Detection Using YoloV5
Jirarat Ieamsaard, Surapon Nathanael Charoensook, Suchart Yammen
Abstract
Ongoing Corona virus disease 2019 (Covid19) pandemic, face mask wearing in public could reduce number of COVID-19 infected by minimizing the release of respiratory droplet from infected people. This paper is to study an effective method for face mask detection using a deep learning model created by "Yolov5". Comparative model developed with a different number of epochs: 20, 50, 100, 300 and 500. The experimental results show that the deep learning model created with 300 epochs has the highest performance with an accuracy of 96.5%.
Topics & Concepts
Deep learningFace (sociological concept)Coronavirus disease 2019 (COVID-19)Computer scienceArtificial intelligenceFace detectionFacial recognition systemMachine learningComputer visionPattern recognition (psychology)MedicineDiseaseInfectious disease (medical specialty)SociologyPathologySocial scienceFace recognition and analysisInfection Control and VentilationCOVID-19 Pandemic Impacts