Deep learning-based occlusion-aware face mask detection for airborne disease control

Abstract Airborne infectious diseases are a significant threat to human beings. Nowadays, one of the deadliest airborne diseases, coronavirus (COVID-19), is resulting in a massive health crisis due to its rapid transmission. The World Health Organization for protection against the spread of airborne diseases has set several guidelines. The most effective preventive measure against airborne diseases, according to the World Health Organization, is wearing masks in public places and crowded areas. It is challenging to monitor people manually in these areas. In this study, we collect data from public and local sources to develop an occlusion-aware face mask detection model. This study presents a deep learning-based occlusion-aware face mask detection model designed to identify both proper and improper mask usage, even under partial facial occlusions. A dataset of 4,820 images, including occlusions from hands, objects, and mask misuse, was used to train and evaluate three convolutional neural network models: InceptionV3, MobileNetV2, and DenseNet121. Among them, DenseNet121 achieved the highest accuracy of 96.3% on test data. Therefore, our proposed study is used to investigate occlusion aware face mask classification using deep learning.