# Real-time Mask Detection on Google Edge TPU

arXiv: https://arxiv.org/abs/2010.04427 · HTML: https://mlsanity.com/paper/2010.04427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Learning on Edge TPUs](https://mlsanity.com/paper/2108.13732/) (2021)
- [Vision Controlled Sensorized Prosthetic Hand](https://mlsanity.com/paper/2407.12807/) (2024)
- [Fully Quantized Always-on Face Detector Considering Mobile Image Sensors](https://mlsanity.com/paper/2311.01001/) (2023)

## Abstract (arXiv)

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.