TY  - JOUR
A1  - Benifa, J. V. Bibal
A1  - Chola, Channabasava
A1  - Muaad, Abdullah Y.
A1  - Hayat, Mohd Ammar Bin
A1  - Bin Heyat, Md Belal
A1  - Mehrotra, Rajat
A1  - Akhtar, Faijan
A1  - Hussein, Hany S.
A1  - Ramírez-Vargas, Debora L.
A1  - Kuc Castilla, Ángel Gabriel
A1  - Díez, Isabel de la Torre
A1  - Khan, Salabat
ID  - unic7793
JF  - Sensors
SN  - 1424-8220
UR  - http://doi.org/10.3390/s23136090
AV  - public
Y1  - 2023/07//
VL  - 23
TI  - FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas
N2  - 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.
IS  - 13
KW  - artificial intelligence; COVID-19; deep learning; FaceMask; MobileNetV2; pandemic; SARS CoV-2; surveillance; World Health Organization
ER  -