Bania Rubul Kumar
Department of Computer Application, North-Eastern Hill University, Tura Campus, Tura, Meghalaya 794002 India.
Multimed Tools Appl. 2023 Feb 1:1-23. doi: 10.1007/s11042-023-14408-y.
The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask has become an adequate protection solution many governments adopt. Manual real-time monitoring of face mask wearing for many people is becoming a difficult task. This paper applies three heterogeneous deep transfer learning models, viz., ResNet50, Inception-v3, and VGG-16, to prepare an ensemble classification model for detecting whether a person is wearing a mask. The ensemble classification model is underlined by the concept of the weighted average technique. The proposed framework is based on two phases. An off-line phase that aims to prepare a classification model by following training-testing steps to detect and locate facemasks. Then in the second online phase, it is deployed to detect real-time faces from live videos, which are captured by a web-camera. The prepared model is compared with several state-of-the-art models. The proposed model has achieved the highest classification accuracy of 99.97%, precision of 0.997, recall of 0.997, F1-score of 0.997 and kappa coefficient 0.994. The superiority of the model over state-of-the-art compared methods is well evident from the experimental results.
新冠疫情正在引发一场全球健康危机。公共场所需要免受这场疫情的不利影响。佩戴口罩已成为许多政府采用的一种适当的防护解决方案。对许多人进行口罩佩戴情况的人工实时监测正成为一项艰巨的任务。本文应用三种异构深度迁移学习模型，即ResNet50、Inception-v3和VGG-16，来制备一个用于检测人员是否佩戴口罩的集成分类模型。该集成分类模型以加权平均技术的概念为基础。所提出的框架基于两个阶段。一个离线阶段，旨在通过遵循训练-测试步骤来制备一个分类模型，以检测和定位口罩。然后在第二个在线阶段，将其部署以从网络摄像头捕获的实时视频中检测实时人脸。将所制备的模型与几个最先进的模型进行比较。所提出的模型实现了99.97%的最高分类准确率、0.997的精确率、0.997的召回率、0.997的F1分数和0.994的kappa系数。实验结果充分证明了该模型相对于最先进的比较方法的优越性。
