Kaviya P, Chitra P, Selvakumar B
Kamaraj College of Engineering and Technology, Vellakulam and 625701, Tamilnadu, India.
Thiagarajar College of Engineering, Madurai and 625015, Tamilnadu, India.
Procedia Comput Sci. 2023;218:1561-1570. doi: 10.1016/j.procs.2023.01.134. Epub 2023 Jan 31.
Corona Virus Disease 2019 (COVID-19) is caused by Severe Acute Syndrome Corona Virus 2 (SARS-COV-2). It has become a pandemic disease of the 21st century, killing many lives. During this pandemic situation, precautious measures like social distancing and wearing face mask are being followed globally to break the COVID chain. A pre-programmed viewing system is needed to monitor whether these COVID-19 appropriate behaviours are being followed by the commoners and to ensure COVID-19 preventive measures are followed appropriately. In this work, a deep learning based predictive model and live risk analysis application has been proposed, which detects the high-risk prone areas based on social distancing measures among individuals and face mask wearing tendency of the commoners. The proposed system utilizes ImageNet-1000 dataset for human detection using You Only Look Once (YOLOv3) object detection algorithm; Residual Neural Network (ResNet50v2) uses Kaggle dataset and Real-World Masked Face Dataset (RMFD) for detecting if the persons are face masked or not. Detected human beings (in side-view) are transformed to top view using Top-View Transform Model (TVTM) followed by the calculation of interpersonal distance between the pedestrians and categorized them into three classes include high risk, medium risk, low risk. This unified predictive model provided an accuracy of 97.66%, precision of 97.84%, and F1-Score of 97.92%.
2019冠状病毒病（COVID-19）由严重急性呼吸综合征冠状病毒2（SARS-COV-2）引起。它已成为21世纪的大流行病，夺走了许多生命。在这种大流行情况下，全球都在采取社交距离和戴口罩等预防措施来阻断新冠病毒传播链。需要一个预编程的监测系统来监控普通民众是否遵守这些针对COVID-19的适当行为，并确保适当遵循COVID-19预防措施。在这项工作中，提出了一种基于深度学习的预测模型和实时风险分析应用程序，该程序根据个体之间的社交距离措施和普通民众戴口罩的倾向来检测高风险区域。所提出的系统利用ImageNet-1000数据集，使用“你只看一次”（YOLOv3）目标检测算法进行人体检测；残差神经网络（ResNet50v2）使用Kaggle数据集和真实世界蒙面人脸数据集（RMFD）来检测人员是否戴口罩。使用顶视图变换模型（TVTM）将检测到的人体（侧视图）转换为顶视图，然后计算行人之间的人际距离，并将他们分为高风险、中风险、低风险三类。这个统一的预测模型的准确率为97.66%，精确率为97.84%，F1分数为97.92%。
