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Predictions of hazardous plastic waste amounts based on disposable face mask wearing habits

Cesoniene Laima, Simanaviciute Raimonda, Sileikiene Daiva, Bedla Dawid

Journal of Water and Land Development

2023

no. 59

275--282

Since March 11, 2020, the global community has faced the challenges of the COVID-19 pandemic. In response, numerous countries, including the Republic of Lithuania, mandated the wearing of face masks to curb the virus’s spread. Yet, a section of the Lithuanian populace resisted this move, voicing concerns about the inconvenience of mask-wearing and potential privacy infringements. These concerns endured, even amidst debates on the masks’ effectiveness. This article explores how the Lithuanian public responded to mask-wearing protocols during the pandemic. Survey analysis highlighted a troubling trend: many individuals dispose of face masks with their regular trash, often without proper packaging. Most masks are sourced from pharmacies or are provided by employers and are typically thrown away after just one day of use. The data underscores a significant knowledge gap in correct mask disposal, as a significant portion ends up mingled with general household waste, without proper containment. Moreover, many people keep used masks in pockets or bags. Notably, during the pandemic, an estimated 2 mln adult Lithuanians may have generated roughly 15.24 Mg of hazardous plastic waste through mask disposal.

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

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

Annals of Computer Science and Information Systems

2021

Vol. 27

71--76

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}