# COVID-19 personal protective equipment detection using real-time deep learning methods

arXiv: https://arxiv.org/abs/2103.14878 · HTML: https://mlsanity.com/paper/2103.14878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 27 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques](https://mlsanity.com/paper/2005.01385/) (2020)

## Abstract (arXiv)

The exponential spread of COVID-19 in over 215 countries has led WHO to recommend face masks and gloves for a safe return to school or work. We used artificial intelligence and deep learning algorithms for automatic face masks and gloves detection in public areas. We investigated and assessed the efficacy of two popular deep learning algorithms of YOLO (You Only Look Once) and SSD MobileNet for the detection and proper wearing of face masks and gloves trained over a data set of 8250 images imported from the internet. YOLOv3 is implemented using the DarkNet framework, and the SSD MobileNet algorithm is applied for the development of accurate object detection. The proposed models have been developed to provide accurate multi-class detection (Mask vs. No-Mask vs. Gloves vs. No-Gloves vs. Improper). When people wear their masks improperly, the method detects them as an improper class. The introduced models provide accuracies of (90.6% for YOLO and 85.5% for SSD) for multi-class detection. The systems' results indicate the efficiency and validity of detecting people who do not wear masks and gloves in public.