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.
Cite
@article{arxiv.2010.04427,
title = {Real-time Mask Detection on Google Edge TPU},
author = {Keondo Park and Wonyoung Jang and Woochul Lee and Kisung Nam and Kihong Seong and Kyuwook Chai and Wen-Syan Li},
journal= {arXiv preprint arXiv:2010.04427},
year = {2020}
}