  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.
