With the recent outbreak and rapid transmission of the COVID-19 pandemic, the need for the public to follow social distancing norms and wear masks in public is only increasing. According to the World Health Organization, to follow proper social distancing, people in public places must maintain at least 3ft or 1m distance between each other. This project focuses on a solution to help enforce proper social distancing and wearing masks in public using deep learning object detection on video footage and images in real time. The experimental results shown in this project infer that the detection of masked faces and human subjects based on deep learning has stronger robustness and faster detection speed as compared to its competitors. The network ensures inference speed capable of delivering real-time results without compromising on accuracy, even in complex setups. The social distancing method proposed also yields promising results in several variable scenarios.