An automated AI and Video Measurement Techniques for Monitoring Social Distancing, Mask Detection, and Facial Temperature Screening for COVID-19

COVID-19 is an infectious virus caused by acute respiratory syndrome SARS-CoV-2. It was first discovered in December 2019 in Wuhan, China. This ongoing pandemic caused infected cases, including many deaths around the world. Coronavirus is spread mainly by air droplets near the infected person due to sneezing, coughing, and talking. Pretrained DL models utilize large CNN layers, which require more disk size on IoTembedded devices and affect real-time detection. This research presents an integrated lightweight DL approach for real-time and multi-task (social distancing, mask detection, and facial temperature) video measurement to control the spread of coronavirus among individuals. The three tasks have used the most recent YOLO detectors (YOLOv7-tiny). It is an object detection model optimized based on the original YOLOv7 to simply the neural network architecture. The trained models have been evaluated in terms of mean average precision, Recall, and Precision to assess the algorithm performance. The proposed approach has been deployed and executed on NVIDIA devices (Jetson nano, Jetson Xavier AGX) composed of visible and thermal cameras. The visible camera is used for face mask detection, while the thermal camera is used for facial temperature measurement and social distancing. This research enriched the prevention system of COVID-19 by the integrated approach compared to the state-of-the-art methodologies. In addition, we obtained promising results for real-time detection. The proposed approach is suitable for a surveillance system to monitor social distancing, Face mask detection, and measuring the facial temperature among individuals.