COVID-19 personal protective equipment detection using real-time deep learning methods
This addresses the need for automated monitoring of PPE compliance in public spaces during the pandemic, but it is incremental as it applies existing deep learning methods to a new domain.
The paper tackled the problem of detecting personal protective equipment (PPE) like face masks and gloves in public areas to help curb COVID-19 spread, achieving accuracies of 90.6% for YOLOv3 and 85.5% for SSD MobileNet in multi-class detection.
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.