基于Google Edge实时的掩码检测TPU
Real-time Mask Detection on Google Edge TPU
摘要：
COVID-19爆发后，自动检测人们是否戴口罩以降低一线工作人员的风险变得非常重要。此外，在本地处理用户数据是解决隐私和网络带宽问题的好方法。在本文中，我们提出了一个轻量级模型，用于检测特定区域的人是否佩戴口罩，该模型也可以部署在Coral Dev Board上，Coral Dev Board是一种包含Google Edge TPU的商用开发板。我们的方法结合了基于MobileNetV 2加SSD的对象检测网络和仅限整数硬件的量化方案。因此，Edge TPU中更轻的模型具有更低的延迟，更适合实时的【形】执行，同时保持与浮点器件相当的精度。
英文摘要：
After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing masks in order to reduce risk of front-line workers. In addition, processing user data locally is a great way to address both privacy and network bandwidth issues. In this paper, we present a light-weighted model for detecting whether people in a particular area wear masks, which can also be deployed on Coral Dev Board, a commercially available development board containing Google Edge TPU. Our approach combines the object detecting network based on MobileNetV2 plus SSD and the quantization scheme for integer-only hardware. As a result, the lighter model in the Edge TPU has a significantly lower latency which is more appropriate for real-time execution while maintaining accuracy comparable to a floating point device.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
