In response to the global COVID-19 pandemic, there has been a critical demand for protective measures, with face masks emerging as a primary safeguard. The approach involves a two-fold strategy: first, recognizing the presence of a face by detecting faces, and second, identifying masks on those faces. This project utilizes deep learning to create a model that can detect face masks in real-time streaming video as well as images. Face detection, a facet of object detection, finds applications in diverse fields such as security, biometrics, and law enforcement. Various detector systems worldwide have been developed and implemented, with convolutional neural networks chosen for their superior performance accuracy and speed in object detection. Experimental results attest to the model's excellent accuracy on test data. The primary focus of this research is to enhance security, particularly in sensitive areas. The research paper proposes a rapid image pre-processing method with masks centred on faces. Employing feature extraction and Convolutional Neural Network, the system classifies and detects individuals wearing masks. The research unfolds in three stages: image pre-processing, image cropping, and image classification, collectively contributing to the identification of masked faces. Continuous surveillance through webcams or CCTV cameras ensures constant monitoring, triggering a security alert if a person is detected without a mask.

翻译：针对全球新冠肺炎疫情，防护措施需求极为迫切，其中口罩成为首要防护手段。本研究采用双重策略：首先通过面部检测识别面部存在，其次检测这些面部是否佩戴口罩。本项目利用深度学习构建模型，能够实时检测流媒体视频及图像中的人员口罩佩戴情况。面部检测作为物体检测的分支，在安保、生物识别、执法等多个领域具有广泛应用。全球已开发并部署多种检测系统，其中卷积神经网络因其在物体检测中卓越的精度与速度优势而被选用。实验结果表明，该模型在测试数据上具有优异精度。本研究核心目标在于增强特定敏感区域的安全性。论文提出一种快速图像预处理方法，以面部为中心设置口罩区域。系统通过特征提取与卷积神经网络，对佩戴口罩与未佩戴口罩者进行分类与检测。研究分图像预处理、图像裁剪、图像分类三个阶段，共同实现口罩佩戴者的识别。通过网络摄像头或闭路电视摄像机持续监控，若检测到未佩戴口罩者即触发安全警报。