A multi-class framework for face mask compliance detection using lightweight deep learning models.
Overview
Abstract
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Data
Datasets cited
- kaggle.com/
datasets/ , at Kaggle; found in “Data availability”andrewmvd
Data availability statement
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- it points to a dataset: kaggle.com/
datasets/ andrewmvd - it says that the data are available on request
Read it in the paper: doi.org/10.1038/s41598-026-50603-1.
Versions
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 11 keywords, 5 MeSH terms, 1 funder, 26 references.
Cite
This paper
E, B., P, M., M, S., & J, O. (2026). A multi-class framework for face mask compliance detection using lightweight deep learning models. Scientific reports, 16(1), 22059. https://
BibTeX
@article{e2026multi,
author = {E, Balraj and P, Manikandan and M, Sambath and J, Omana},
title = {{A multi-class framework for face mask compliance detection using lightweight deep learning models}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {22059},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/
url = {https://
pmid = {42156444},
pmcid = {PMC13369893}
}
RIS
TY - JOUR
AU - E, Balraj
AU - P, Manikandan
AU - M, Sambath
AU - J, Omana
TI - A multi-class framework for face mask compliance detection using lightweight deep learning models
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/
VL - 16
IS - 1
SP - 22059
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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"container-title-short":
"volume": "16",
"issue": "1",
"page": "22059",
"DOI": "10.1038/
"PMID": "42156444",
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"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://
"language": "en",
"issued": {
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}
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