RRFMDS: Rapid Real-Time Face Mask Detection System for Effective COVID-19 Monitoring

The primary mode of COVID-19 transmission is through respiratory droplets that are produced when an infected person talks, coughs, or sneezes. To avoid the fast spread of the virus, the WHO has instructed people to use face masks in crowded and public areas. This paper proposes the rapid real-time face mask detection system or RRFMDS, an automated computer-aided system to detect a violation of a face mask in real-time video. In the proposed system, single-shot multi-box detector is utilized for face detection, while fine-tuned MobileNetV2 is used for face mask classification. The system is lightweight (low resource requirement) and can be merged with pre-installed CCTV cameras to detect face mask violation. The system is trained on a custom dataset which consists of 14,535 images, of which 5000 belong to incorrect masks, 4789 to with masks, and 4746 to without masks. The primary purpose of creating such a dataset was to develop a face mask detection system that can detect almost all types of face masks with different orientations. The system can detect all three classes (incorrect masks, with mask and without mask faces) with an average accuracy of 99.15% and 97.81%, respectively, on training and testing data. The system, on average, takes 0.14201142 s to process a single frame, including detecting the faces from the video, processing a frame and classification.