# RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2005.03950 · HTML: https://mlsanity.com/paper/2005.03950/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection](https://mlsanity.com/paper/2203.16506/) (2022)
- [A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic](https://mlsanity.com/paper/2405.18387/) (2024)
- [A Masked Face Classification Benchmark on Low-Resolution Surveillance Images](https://mlsanity.com/paper/2211.13061/) (2022)
- [Hybrid Classification-Regression Adaptive Loss for Dense Object Detection](https://mlsanity.com/paper/2408.17182/) (2024)

## Abstract (arXiv)

Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model.