COVID-19 Face Mask Detection with Pre-trained Deep Learning Models
DOI:
https://doi.org/10.5281/zenodo.8071715Anahtar Kelimeler:
Covid-19- Pre-Trained Models- Mask detection- Transfer learningÖz
The global pandemic known as COVID-19 puts huge pressure on researchers to use technological solutions to provide further protection mechanisms. Face masks are one of the most important protection mechanisms among other health protocols. This study detects the wearing mask classification problem using four CNN models: VGG16, ResNet50V2, IncptionV3, and MobileNetV2 based on the Transfer Learning and also makes a fair comparison among their performance. The proposed models enhance the classification of wearing masks into three classes; without the mask, the correct wearing of the mask, and not the correct wearing of the mask. The four-transfer learning models of CNN architectures were used to train, test, and validate based on the image dataset. The results reveal that the proposed models have performed the classification task to detect the condition of wearing a mask. VGG16, ResNet50V2, and MobileNetV2 models achieved the same accuracy level of 99%, while the IncptionV3 achieved a little bit lower accuracy at 97%.
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