Ramadevi Potharla, Das Raja
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
Heliyon. 2024 Aug 8;10(16):e35928. doi: 10.1016/j.heliyon.2024.e35928. eCollection 2024 Aug 30.
Over the last several years, the COVID-19 epidemic has spread over the globe. People have become used to the novel standard, which involves working from home, chatting online, and keeping oneself clean, to stop the spread of COVID-19. Due to this, many public spaces make an effort to make sure that their visitors wear proper face masks and maintain a safe distance from one another. It is impossible for monitoring workers to ensure that everyone is wearing a face mask; automated solutions are a far better option for face mask identification and monitoring to assist control public conduct and reduce the COVID-19 epidemic. The motivation for developing this technology was the need to identify those individuals who uncover their faces. Most of the previously published research publications focused on various methodologies. This study built new methods namely K-medoids, K-means, and Fuzzy K-Means(FKM) to use image pre-processing to get the better quality of the face and reduce the noise data. In addition, this study investigates various machine learning models Convolutional neural networks (CNN) with pre-trained (DenseNet201, VGG-16, and VGG-19) models, and Support Vector Machine (SVM) for the detection of face masks. The experimental results of the proposed method K-medoids with pre-trained model DenseNet201 achieved the 97.7 % accuracy best results for face mask identification. Our research results indicate that the segmentation of images may improve the identification of accuracy. More importantly, the face mask identification tool is more beneficial when it can identify the face mask in a side-on approach.
在过去几年里，新冠疫情已在全球蔓延。人们已习惯了这种新的规范，即居家办公、在线聊天以及保持个人清洁，以阻止新冠病毒的传播。因此，许多公共场所都努力确保其访客佩戴合适的口罩，并相互保持安全距离。监测人员不可能确保每个人都戴着口罩；自动化解决方案对于口罩识别和监测来说是更好的选择，有助于控制公众行为并减少新冠疫情。开发这项技术的动机是需要识别那些摘下口罩的人。此前发表的大多数研究文献都集中在各种方法上。本研究构建了新的方法，即K-中心点算法、K-均值算法和模糊K-均值算法（FKM），以利用图像预处理来获得更高质量的面部图像并减少噪声数据。此外，本研究还研究了各种机器学习模型，包括带有预训练模型（DenseNet201、VGG-16和VGG-19）的卷积神经网络（CNN）以及用于检测口罩的支持向量机（SVM）。所提出的K-中心点算法与预训练模型DenseNet201相结合的方法，在口罩识别实验中取得了97.7%的最佳准确率。我们的研究结果表明，图像分割可能会提高识别准确率。更重要的是，当口罩识别工具能够从侧面识别口罩时，它会更有用。
