TY - GEN
T1 - Smart Robotic System to Fight the Spread of COVID-19 at Construction Sites
AU - Seagers, Jonathan
AU - Liu, Yizhi
AU - Jebelli, Houtan
N1 - Publisher Copyright:
© 2022 ASCE.
PY - 2022
Y1 - 2022
N2 - To prevent the spread of the COVID-19 virus at construction sites, accounting for the surveillance and precautions imposed by the pandemic means frequent contact tracing, symptom monitoring, and PPE reminders. During the pandemic, many construction projects continued because of the need for a priority building that was under construction, such as for a healthcare project. To promote safety under unprecedented circumstances, this paper proposes an intelligent robotic surveillance system that can locate workers and identify whether they are properly wearing masks. The system leverages autonomous terrestrial robots equipped with wide-angle cameras to capture images of their surroundings and a two-dimensional laser scanner for simultaneous localization and mapping (SLAM). In this system, the robot is equipped with precise image processing in a well-trained convolutional neural network (VGG16) to recognize workplace entities, particularly workers and their masks, with 83.3% accuracy. Simultaneously, the mounted laser scanner enables the robot to generate the map of the surrounding environment based on the near-real-time Hector SLAM algorithm. In situ recognition would help track workers who improperly use masks and trigger interventions that would diminish the spread of the virus at the construction site. The proposed robotic system will non-intrusively and privately inform workers to use proper protocol to protect them from COVID-19 and other deadly viruses, thereby improving health and safety.
AB - To prevent the spread of the COVID-19 virus at construction sites, accounting for the surveillance and precautions imposed by the pandemic means frequent contact tracing, symptom monitoring, and PPE reminders. During the pandemic, many construction projects continued because of the need for a priority building that was under construction, such as for a healthcare project. To promote safety under unprecedented circumstances, this paper proposes an intelligent robotic surveillance system that can locate workers and identify whether they are properly wearing masks. The system leverages autonomous terrestrial robots equipped with wide-angle cameras to capture images of their surroundings and a two-dimensional laser scanner for simultaneous localization and mapping (SLAM). In this system, the robot is equipped with precise image processing in a well-trained convolutional neural network (VGG16) to recognize workplace entities, particularly workers and their masks, with 83.3% accuracy. Simultaneously, the mounted laser scanner enables the robot to generate the map of the surrounding environment based on the near-real-time Hector SLAM algorithm. In situ recognition would help track workers who improperly use masks and trigger interventions that would diminish the spread of the virus at the construction site. The proposed robotic system will non-intrusively and privately inform workers to use proper protocol to protect them from COVID-19 and other deadly viruses, thereby improving health and safety.
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U2 - 10.1061/9780784483985.046
DO - 10.1061/9780784483985.046
M3 - Conference contribution
AN - SCOPUS:85128962269
T3 - Construction Research Congress 2022: Health and Safety, Workforce, and Education - Selected Papers from Construction Research Congress 2022
SP - 452
EP - 461
BT - Construction Research Congress 2022
A2 - Jazizadeh, Farrokh
A2 - Shealy, Tripp
A2 - Garvin, Michael J.
PB - American Society of Civil Engineers (ASCE)
T2 - Construction Research Congress 2022: Health and Safety, Workforce, and Education, CRC 2022
Y2 - 9 March 2022 through 12 March 2022
ER -