Alleviation of COVID by means of Social Distancing & Face Mask Detection Using YOLO V4

Abstract

This paper consists of social distancing & face mask detection for the events of coronavirus, alleviation in such pandemic can be solved by social distancing as well as putting on a face mask. This small step of wearing a face mask as well as following social distancing would save lots of lives as the spread of the virus could be mitigated. YOLO stands for You Only Look Once, this algorithm is used for Object Detection as well as Object Tracking, this research uses YOLO for calculating the social distancing & identifying face mask on people's face with the help of Object Detection, whereas tracking the face is done by Object Tracking. The minimum distance to keep while adhering for social distancing is 6 Feet, keeping this as the base for calculating distance, the model was trained and used for object detection as well as for object tracking. There are different types of algorithms available, YOLO stands out from all the other present currently. The custom datasets were used for the understanding the face masks and it was trained on those datasets for detection and tracking. For evaluation of the trained model, mAP (Mean Average Precision) was calculated for both the use cases (Social Distancing & Face Mask Detection), it works by comparing the ground-truth bounding box vs the detected box and, in the end, returns the score. The higher the mAP score would be, the better model is in the detection of objects. Mean Average Precision was calculated for two different thresholds (0.25 % & 0.50 %) with 101 recall points. Three different classes were created for classification those were Good, Bad & None, for which True Positive & False Positive values were calculated with ROC Curve for better understanding.