  The exponential spread of COVID-19 in over 215 countries has led WHO to
recommend face masks and gloves for a safe return to school or work. We used
artificial intelligence and deep learning algorithms for automatic face masks
and gloves detection in public areas. We investigated and assessed the efficacy
of two popular deep learning algorithms of YOLO (You Only Look Once) and SSD
MobileNet for the detection and proper wearing of face masks and gloves trained
over a data set of 8250 images imported from the internet. YOLOv3 is
implemented using the DarkNet framework, and the SSD MobileNet algorithm is
applied for the development of accurate object detection. The proposed models
have been developed to provide accurate multi-class detection (Mask vs. No-Mask
vs. Gloves vs. No-Gloves vs. Improper). When people wear their masks
improperly, the method detects them as an improper class. The introduced models
provide accuracies of (90.6% for YOLO and 85.5% for SSD) for multi-class
detection. The systems' results indicate the efficiency and validity of
detecting people who do not wear masks and gloves in public.
