Mask and Social Distancing Detector for Enhanced Public Safety

This paper presents an enforcement of safety measures such as wearing face masks and maintaining physical distancing, which has become essential in various public places to ensure public health and safety. Despite the significance of these measures, many individuals fail to comply, necessitating automated systems for monitoring and regulation. This work explores using state-of-the-art deep learning models to create an effective face mask and social distancing detection system. The study evaluates, compares, and contrasts various object detection frameworks to identify the most suitable models for these tasks. Techniques such as Convolutional Neural Network (CNN) architectures MobileNetV2 are analyzed alongside singleshot detectors and multiple iterations of the YOLOv4 framework.