Real-time Mask Detection on Google Edge TPU
This addresses mask detection needs for front-line workers and privacy-conscious applications, but is incremental as it adapts existing methods to new hardware.
The paper tackles the problem of real-time mask detection for COVID-19 safety by developing a lightweight model deployable on Google Edge TPU hardware, achieving significantly lower latency suitable for real-time execution while maintaining accuracy comparable to floating-point devices.
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.