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 becomes one of the effective protection solutions adopted by many governments. Manual real-time monitoring of facemask wearing for a large group of people is becoming a difficult task. The goal of this paper is to use deep learning (DL), which has shown excellent results in many real-life applications, to ensure efficient real-time facemask detection. The proposed approach is based on two steps. An off-line step aiming to create a DL model that is able to detect and locate facemasks and whether they are appropriately worn. An online step that deploys the DL model at edge computing in order to detect masks in real-time. In this study, we propose to use MobileNetV2 to detect facemask in real-time. Several experiments are conducted and show good performances of the proposed approach (99% for training and testing accuracy). In addition, several comparisons with many state-of-the-art models namely ResNet50, DenseNet, and VGG16 show good performance of the MobileNetV2 in terms of training time and accuracy.
翻译:COVID-19大流行的COVID-19大流行正在造成全球健康危机。公共空间需要受到保护,免受这一流行病的不利影响。戴面罩是许多国家政府采取的有效保护解决办法之一。手动实时监测一大批人穿戴面罩的工作正成为一个困难的任务。本文的目标是利用深层学习(DL),这在许多实际应用中都显示极好的结果,以确保有效的实时面罩检测。拟议方法基于两个步骤。一个脱线步骤,旨在创建DL模型,能够检测和定位面纱及其是否适当磨损。一个在线步骤,在边缘计算中部署DL模型,以便实时检测面罩。在本研究中,我们提议使用MiveNetV2来实时检测面罩。进行了一些实验,并展示了拟议方法的良好表现(99%用于培训和测试准确性)。此外,与许多最先进的模型(ResNet50、DenseNetNet和VGG16)进行了几次比较,显示在培训时间和VGUPNet2的准确性。