%0 Journal Article
%@ 1424-8220
%A Benifa, J. V. Bibal
%A Chola, Channabasava
%A Muaad, Abdullah Y.
%A Hayat, Mohd Ammar Bin
%A Bin Heyat, Md Belal
%A Mehrotra, Rajat
%A Akhtar, Faijan
%A Hussein, Hany S.
%A Ramírez-Vargas, Debora L.
%A Kuc Castilla, Ángel Gabriel
%A Díez, Isabel de la Torre
%A Khan, Salabat
%D 2023
%F unic:7793
%J Sensors
%K artificial intelligence; COVID-19; deep learning; FaceMask; MobileNetV2; pandemic; SARS CoV-2; surveillance; World Health Organization
%N 13
%P 6090
%T FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas
%U http://repositorio.unic.co.ao/id/eprint/7793/
%V 23
%X A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.