论文标题
多阶段CNN面膜检测的体系结构
Multi-Stage CNN Architecture for Face Mask Detection
论文作者
论文摘要
2019年底见证了2019年冠状病毒病（Covid-19）的爆发，即使在2020年，它仍然是数百万生命和企业的困境的原因。随着世界从大流行中恢复过来，并计划恢复正常状态，恢复正常状态，尤其是所有人，尤其是那些打算恢复预言活动的人。研究证明，戴上面膜大大降低了病毒传播的风险，并提供了一种保护感。但是，手动跟踪该政策的实施是不可行的。技术在这里拥有关键。我们介绍了一个基于深度学习的系统，该系统可以检测不正确使用面罩的实例。我们的系统包括一个双阶段卷积神经网络（CNN）结构，能够检测掩盖和揭示的面孔，并可以与预装的CCTV摄像机集成在一起。这将有助于跟踪违规行为，促进口罩的使用并确保安全的工作环境。
The end of 2019 witnessed the outbreak of Coronavirus Disease 2019 (COVID-19), which has continued to be the cause of plight for millions of lives and businesses even in 2020. As the world recovers from the pandemic and plans to return to a state of normalcy, there is a wave of anxiety among all individuals, especially those who intend to resume in-person activity. Studies have proved that wearing a face mask significantly reduces the risk of viral transmission as well as provides a sense of protection. However, it is not feasible to manually track the implementation of this policy. Technology holds the key here. We introduce a Deep Learning based system that can detect instances where face masks are not used properly. Our system consists of a dual-stage Convolutional Neural Network (CNN) architecture capable of detecting masked and unmasked faces and can be integrated with pre-installed CCTV cameras. This will help track safety violations, promote the use of face masks, and ensure a safe working environment.
