Densely populated regions face masks localization and classification using deep learning models

• Autorzy: Pham-Hoang-Nam Anh , Phung-Khanh Linh , Vi Le-Thi-Tuong , Ly-Tu Nga

Identyfikatory

DOI

10.15439/2021R13

• Konferencja: Sixth International Conference on Research in Intelligent and Computing
• Języki publikacji: EN

Abstrakty

EN

Over the last year, the correct wearing of facial masks in public is still a relevant matter in the fight against the COVID-19 pandemic. A popular approach that helps regulate the situation by global researchers is building smart systems for face mask detection. Following such spirit, this paper will contribute to the literature in two main aspects: \\ (1) We first propose a new face mask detector model using the state-of-the-art RetinaFace for face localization in populous regions and the ResNet50V1 classifier to group the faces under 3 categories: correctly-worn, incorrectly-worn and no-masks-worn. \\ (2) In order to select the ResNet50V1 as the backbone for the final model, we also analyzed its performance in accordance with another 3 classifiers on a face mask dataset beforehand. Performance metrics from the test phase have shown that our detector achieved the best accuracy among all the works compared, with $94,59$\\% on one test dataset and a less satisfactory $69.6$\\% on another due to certain characteristics of the set. The code is available at: \url{https://github.com/barbatoz0220/Densely-populated-FMD.git}

Słowa kluczowe

EN

dense population regions; face mask; localization; classification; Covid-19; deep learning; MobileNet; ResNet,; AIZOO; neuralet

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 27
• Strony: 71--76
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Pham-Hoang-Nam Anh

• School of Computer Science and Engineering International University, Ho Chi Minh City, Vietnam

autor

Phung-Khanh Linh

• School of Computer Science and Engineering International University, Ho Chi Minh City, Vietnam

autor

Vi Le-Thi-Tuong

• School of Computer Science and Engineering International University, Ho Chi Minh City, Vietnam

autor

Ly-Tu Nga

• School of Computer Science and Engineering International University, Ho Chi Minh City, Vietnam

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).