Face Masks Usage Monitoring for Public Health Security using Computer Vision on Hardware

Abstract

Wearing face masks is one of the direct measures that can help tackling the spread of the new coronavirus. In this paper we presented an architecture for face mask wearing detection using pre-trained deep learning models for computer vision and implementation of them on embedded hardware platforms. Three object detection models were fine-tuned and optimized to run on 4 different hardware platforms. The fine tuning and optimization of the models resulted in significant reduction of the inference time, thus making the use of this technology in IoT based security systems for real-time automatic monitoring of face masks wearing realisable.