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

Moein Razavi; Hamed Alikhani; Vahid Janfaza; Benyamin Sadeghi; Ehsan Alikhani

doi:10.1007/s42979-021-00894-0

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

SN Comput Sci. 2022;3(1):27. doi: 10.1007/s42979-021-00894-0. Epub 2021 Oct 29.

Authors

Moein Razavi^#¹, Hamed Alikhani^#², Vahid Janfaza¹, Benyamin Sadeghi³, Ehsan Alikhani⁴

Affiliations

¹ Department of Computer Science and Engineering, Texas A&M University, College Station, Texas USA.
² Department of Engineering, Texas A&M University, College Station, Texas USA.
³ Industrial and Manufacturing Engineering, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin USA.
⁴ Systems Science and Industrial Engineering State University of New York at Binghamton, Binghamton, New York USA.

^# Contributed equally.

Abstract

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. In this paper, we 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, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we 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, we 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. We 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.

Keywords: Construction Sites; Deep Learning; Facemask Detection; Faster RCNN; Social Distance Detection.