AI Article Synopsis
- Face masks are crucial for reducing the spread of COVID-19, prompting the creation of systems to detect mask-wearing individuals.
- It's vital to differentiate between various mask types, as not all provide the same level of protection against viruses.
- This study uses four advanced deep learning models to classify different mask types (KN95, N95, surgical, cloth), finding that ResNet101v2 and ResNet152v2 are the most accurate.
Article Abstract
The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222491 | PMC |
| http://dx.doi.org/10.1016/j.asoc.2022.109207 | DOI Listing |
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