A Real-Time Deep Learning-Based Facial Mask Detection System for Preventing the Transmission of Respiratory Viruses

In order to prevent the spread of respiratory viruses like COVID-19 and influenza, an effective protection method is to wear facial masks in densely populated areas. This has led to a growing need for smart services that automatically detect facial masks and replace manual reminding. To address this challenging task and to contribute towards health safety, this paper introduces MedNetV2 system, which is an efficient deep learning-based facial mask detector with a low computational cost. In comparison with existing systems, the main specificities of the proposed system are: (1) the adoption of an effective deep learning-based framework to deal with both the large scale diversity and position variations of masked faces involved in natural scenes, (2) the interaction between face localization and facial mask detection modules to achieve the overall system goal, (3) the lightweight design and the real-time response well suited for real-world scenarios. Extensive experiments on public dataset and real-world video streaming are carried out to validate quantitatively and visually the effectiveness of the proposed system. Promising results, in terms of detection accuracy as well as time response, are achieved when compared it with other state-of-the-art systems.