{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:31:19Z","timestamp":1782516679017,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,2]],"date-time":"2023-07-02T00:00:00Z","timestamp":1688256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at King Khalid University","award":["RGP.2\/162\/44"],"award-info":[{"award-number":["RGP.2\/162\/44"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23136090","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:53:16Z","timestamp":1688345596000},"page":"6090","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas"],"prefix":"10.3390","volume":"23","author":[{"given":"J. V. Bibal","family":"Benifa","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam 686635, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7509-9354","authenticated-orcid":false,"given":"Channabasava","family":"Chola","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam 686635, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8304-9261","authenticated-orcid":false,"given":"Abdullah Y.","family":"Muaad","sequence":"additional","affiliation":[{"name":"Department of Studies in Computer Science, Mysore University, Manasagangothri, Mysore 570006, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd Ammar Bin","family":"Hayat","sequence":"additional","affiliation":[{"name":"M.A.H. 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