Embedded Implementation of Social Distancing Detector Based on One Stage Convolutional Neural Network Detector

The recent COVID-19 is a very dangerous disease that intimidates humanity. It spreads very fast and many rules must be respected to reduce its prevalence. One of the most important rules is the social distance which means keeping a safe distance between two persons. A safe distance must be one meter or more. Respecting such rules in public spaces is a very challenging task that needs the assistance of artificial intelligence tools. In this paper, we propose a social distance detector using convolutional neural networks. The detector was based on the Yolo model with a custom-made backbone to guarantee real-time processing and embedded implementation. The backbone was optimized to make it suitable for embedded resources. The inference model was evaluated on the Pynq platform. The model was trained and fine-tuned using the MS COCO dataset. The evaluation of the proposed model proved its efficiency with a precision of 87.98% while running in real-time. The achieved results proved the efficiency of the proposed model and the proposed optimization for embedded implementation.