# An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2101.01373 · HTML: https://mlsanity.com/paper/2101.01373/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020)
- [Face Detection with the Faster R-CNN](https://mlsanity.com/paper/1606.03473/) (2016)
- [Fast Multi-language LSTM-based Online Handwriting Recognition](https://mlsanity.com/paper/1902.10525/) (2019)
- [DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic](https://mlsanity.com/paper/2008.11672/) (2020)

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

- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio](https://mlsanity.com/paper/2111.12888/) (2021)
- [Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning](https://mlsanity.com/paper/2106.12766/) (2021)
- [COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics](https://mlsanity.com/paper/2208.04704/) (2022)

## Abstract (arXiv)

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. This paper developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, the paper collected and annotated 1,000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1,853 images. Then trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, the paper employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. The paper also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.