Globally, numerous preventive measures were taken to treat the COVID-19 epidemic. Face masks and social distancing were two of the most crucial practices for limiting the spread of novel viruses. With YOLOv5 and a pre-trained framework, we present a novel method of complex mask detection. The primary objective is to detect complex different face masks at higher rates and obtain accuracy of about 94% to 99% on real-time video feeds. The proposed methodology also aims to implement a structure to detect social distance based on a YOLOv5 architecture for controlling, monitoring, accomplishing, and reducing the interaction of physical communication among people in the day-to-day environment. In order for the framework to be trained for the different crowd datasets from the top, it was trained for the human contrasts. Based on the pixel information and the violation threshold, the Euclidean distance between peoples is determined as soon as the people in the video are spotted. In the results, this social distance architecture is described as providing effective monitoring and alerting.
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