未明确命名

本研究使用的数据集未明确命名，由R.V.学院工程系创建，主要包含从监控视频中提取的面部图像，分为戴口罩和不戴口罩两类。数据集通过图像增强技术进行了扩充，包括添加口罩和应用模糊滤镜以模拟监控视频的低质量图像。该数据集旨在支持COVID-19监控系统，通过自动化检测社交距离违规和口罩佩戴情况，以减少病毒传播。

The dataset used in this study, which was not explicitly named, was created by the Department of Engineering of R.V. College. It mainly contains facial images extracted from surveillance videos, categorized into two classes: those wearing face masks and those not wearing face masks. The dataset was augmented via image enhancement techniques, including adding simulated masks and applying blur filters to replicate the low-quality imagery typical of surveillance videos. This dataset is designed to support COVID-19 surveillance systems by automating the detection of social distancing violations and mask-wearing status, thereby reducing viral transmission.