eprintid: 7793 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/77/93 datestamp: 2023-07-03 23:30:08 lastmod: 2023-07-03 23:30:10 status_changed: 2023-07-03 23:30:08 type: article metadata_visibility: show creators_name: Benifa, J. V. Bibal creators_name: Chola, Channabasava creators_name: Muaad, Abdullah Y. creators_name: Hayat, Mohd Ammar Bin creators_name: Bin Heyat, Md Belal creators_name: Mehrotra, Rajat creators_name: Akhtar, Faijan creators_name: Hussein, Hany S. creators_name: Ramírez-Vargas, Debora L. creators_name: Kuc Castilla, Ángel Gabriel creators_name: Díez, Isabel de la Torre creators_name: Khan, Salabat creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: debora.ramirez@unini.edu.mx creators_id: creators_id: creators_id: title: FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: artificial intelligence; COVID-19; deep learning; FaceMask; MobileNetV2; pandemic; SARS CoV-2; surveillance; World Health Organization abstract: 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. date: 2023-07 date_type: published publication: Sensors volume: 23 number: 13 pagerange: 6090 id_number: doi:10.3390/s23136090 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s23136090 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Benifa, J. V. Bibal; Chola, Channabasava; Muaad, Abdullah Y.; Hayat, Mohd Ammar Bin; Bin Heyat, Md Belal; Mehrotra, Rajat; Akhtar, Faijan; Hussein, Hany S.; Ramírez-Vargas, Debora L.; Kuc Castilla, Ángel Gabriel; Díez, Isabel de la Torre y Khan, Salabat mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas. Sensors, 23 (13). p. 6090. ISSN 1424-8220 document_url: http://repositorio.unini.edu.mx/id/eprint/7793/1/sensors-23-06090.pdf