# A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

arXiv: https://arxiv.org/abs/2011.14858 · HTML: https://mlsanity.com/paper/2011.14858/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Tiny Machine Learning: Progress and Futures](https://mlsanity.com/paper/2403.19076/) (2024)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Intelligence at the Extreme Edge: A Survey on Reformable TinyML](https://mlsanity.com/paper/2204.00827/) (2022)
- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices](https://mlsanity.com/paper/2106.01739/) (2021)
- [Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs](https://mlsanity.com/paper/2111.15481/) (2021)
- [The Need and Status of Sea Turtle Conservation and Survey of Associated Computer Vision Advances](https://mlsanity.com/paper/2107.14061/) (2021)
- [Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks](https://mlsanity.com/paper/2107.14070/) (2021)
- [Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations](https://mlsanity.com/paper/2112.06428/) (2021)

## Abstract (arXiv)

The world is going through one of the most dangerous pandemics of all time with the rapid spread of the novel coronavirus (COVID-19). According to the World Health Organisation, the most effective way to thwart the transmission of coronavirus is to wear medical face masks. Monitoring the use of face masks in public places has been a challenge because manual monitoring could be unsafe. This paper proposes an architecture for detecting medical face masks for deployment on resource-constrained endpoints having extremely low memory footprints. A small development board with an ARM Cortex-M7 microcontroller clocked at 480 Mhz and having just 496 KB of framebuffer RAM, has been used for the deployment of the model. Using the TensorFlow Lite framework, the model is quantized to further reduce its size. The proposed model is 138 KB post quantization and runs at the inference speed of 30 FPS.