Predict the Face-Mask-and-Social-Distance Identification by Using Yolo and CNN

Abstract: COVID-19 virus is still a source of concern and hazard in today's world. With such a huge population traveling, manual monitoring of social distance standards is impracticable. Regarding and with a task force and resources that are insufficient to they should be administered There is a requirement for a lightweight, durable, and reliable device. This procedure is automated by a video observation device that operates 24 hours a day, seven days a week. This study offers a detailed and practical solution to the problem. Perform person detection, social distance violation detection, and social distancing violation detection. Using an item, detect faces and classify face masks. Convolution Neural Networks, detection, and grouping (CNN)is a binary classifier that is based on. YOLOv3, Density-fundamentally based spatial bunching of bundles with commotion (DBSCAN), and double are a portion of the instruments that can assist with this. Shot Face Detector (DSFD) and Binary MobileNetV2 Surveillance video assortments were utilized to prepare a classifier. This publication also includes a comparison of various facial types. Face mask detection and classification models. Finally, to make amends for the absence of a dataset within the network, a video dataset labeling technique is presented, couple with a labeled video dataset, which is utilized to evaluate the system. The framework's presentation is estimated as far as precision, F1 score, and gauge time, which should be least for practical use. On the labeled video data set, the device has an accuracy of 91.2 percent and an F1 rating of 90. Seventy-nine percent, with an average forecast time of seven.12 seconds for seventy eight frames of video