Real time feature extraction deep-cnn for mask detection

Abstract

COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique using a single camera, which presents real-time mask detection in closed places. Our contributions are as follows: 1) presenting a real time feature extraction module to improve the detection computational time; 2) enhancing the extracted features learned from the deep convolutional neural network models to improve small objects detection. The proposed model is a lightweight backbone CNN which ensures real time mask detection. The accuracy is also enhanced by utilizing the feature enhancement module after some of the convolution layers in the CNN. We performed extensive experiments comparing our model to the single-shot detector (SDD) and YoloV3 neural network models, which are the state-of-the-art models in the literature. The comparison shows that the result of our proposed model achieves 95.9% accuracy which is 21% higher than SSD and 17.7% higher than YoloV3 accuracy. We also conducted experiments testing the mask detection speed. It was found that our model achieves average detection time of 0.85s for images of size 1024 × 1024 pixels, which is better than the speed achieved by SSD but slightly less than the speed of YoloV3.