论文标题
用于资源约束终点的医疗面罩检测的微小CNN架构
A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints
论文作者
论文摘要
随着新颖的冠状病毒（Covid-19）的快速传播，世界正在经历有史以来最危险的大流行之一。根据世界卫生组织的说法，挫败冠状病毒传播的最有效方法是戴上医疗面罩。监视在公共场所使用口罩的使用一直是一个挑战，因为手动监控可能不安全。本文提出了一种用于检测医疗面罩的体系结构，以在资源受限的端点上部署具有极低的内存足迹。一个带有ARM Cortex-M7微控制器的小型开发板以480 MHz的速度计时，仅具有496 kb的Framebuffer RAM，已用于部署该模型。使用Tensorflow Lite框架，对模型进行量化以进一步降低其尺寸。提出的模型是量化后的138 kb，并以30 fps的推理速度运行。
The world is going through one of the most dangerous pandemics of all time with the rapid spread of the novel coronavirus (COVID-19). According to the World Health Organisation, the most effective way to thwart the transmission of coronavirus is to wear medical face masks. Monitoring the use of face masks in public places has been a challenge because manual monitoring could be unsafe. This paper proposes an architecture for detecting medical face masks for deployment on resource-constrained endpoints having extremely low memory footprints. A small development board with an ARM Cortex-M7 microcontroller clocked at 480 Mhz and having just 496 KB of framebuffer RAM, has been used for the deployment of the model. Using the TensorFlow Lite framework, the model is quantized to further reduce its size. The proposed model is 138 KB post quantization and runs at the inference speed of 30 FPS.
