An Automated Face-mask Detection System using YOLOv5 for Preventing Spread of COVID-19

Abstract

Object detection systems based on deep learning have been immensely successful incomplex object detection tasks images and have shown potential in a wide range of real-life applicationsincluding the COVID-19 pandemic. One of the key challenges in containing and mitigating the infectionamong the population is to ensure and enforce the proper use of face masks. The objective of this paperis to detect the proper use of facial masks among the urban population in a megacity. In this study, wetrained and validated a new dataset to detect images such as ‘with mask’, ‘without mask’, and ‘masknot in position’ using YOLOv5. The dataset is comprised of 6550 images with the three classes. Thedataset attained a commendable performance accuracy of 95% on mAP. This study can be implementedfor automated scanning for monitoring the proper use of face masks in different settings of public spaces.