Enhanced Surgical Mask Recognition Using EfficientNet Architecture
Автор: Galib Muhammad Shahriar Himel, Md. Masudul Islam
Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp
Статья в выпуске: 5 vol.16, 2024 года.
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The research article presents a robust solution to detect surgical masks using a combination of deep learning techniques. The proposed method utilizes the SAM to detect the presence of masks in images, while EfficientNet is employed for feature extraction and classification of mask type. The compound scaling method is used to distinguish between surgical and normal masks in the data set of 2000 facial photos, divided into 60% training, 20% validation, and 20% testing sets. The machine learning model is trained on the data set to learn the discriminative characteristics of each class and achieve high accuracy in mask detection. To handle the variability of mask types, the study applies various versions of EfficientNet, and the highest accuracy of 97.5% is achieved using EfficientNetV2L, demonstrating the effectiveness of the proposed method in detecting masks of different complexities and designs.
Surgical mask detection, EfficientNet, face mask recognition, face mask detection, surgical mask recognition, machine learning, image processing, COVID-19, Segment Anything Model, Transfer learning
Короткий адрес: https://sciup.org/15019497
IDR: 15019497 | DOI: 10.5815/ijigsp.2024.05.03
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