A Deep learning based approach for Social Distance Monitoring

The COVID-19 pandemic has hit the world at large claiming large number of lives till date leaving us with no solution except maintaining social distancing or washing hands regularly, wearing masks and staying at homes. Social distancing is one of the key aspects to prevent spreading of this virus. It means more of maintaining suitable distance between each other. Artificial intelligence has been used widely for a large number of purposes and as such is one of the key tools used here for implementing this project. The proposed system identifies people who are not suitable distance apart by using object detection and calculating the Euclidian distance between two people. This system would be beneficial to the authorities for alerting people if the situation is serious. © 2021 IEEE.

Face Mask Detection Using Deep Learning

With the spread of coronavirus disease 2019 (COVID-19) pandemic throughout the world, social distancing and using a face mask have become crucial to prevent the spreading of this disease. Our goal is to develop a better way to detect face masks. In this paper, we propose a comparison between all available networks, which is an efficient one-stage face mask detector. The detection scheme follows preprocessing, feature extraction, and classification. The mask detector has been built using deep learning, specifically ResNetV2, as the base pre-trained model upon which we have our own CNN. We use OpenCV’s ImageNet to extract faces from video frames and our trained model to classify if the person is wearing a mask or not. We also propose an object removal algorithm to reject prediction below absolute confidence and accept only predictions above it. For the training purpose, we are using the face mask dataset, which consists of 680 images with mask and 686 images without mask. The results show mask detector has an accuracy of 99.9%. We have also used other pre-trained networks like MobileNetV2 as our base network and compared our results. ResNet50 gives us the state-of-the-art performance of face mask detection, which is higher than other face detectors. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.