Remote proximity monitoring between mobile construction resources using camera-mounted UAVs

Daeho Kim, Meiyin Liu, Sang Hyun Lee, Vineet R. Kamat

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

Struck-by accidents have resulted in a significant number of fatal and nonfatal injuries in the construction industry. As a proactive safety measure against struck-by hazards, the authors present an Unmanned Aerial Vehicle (UAV)-assisted visual monitoring method that can automatically measure proximities among construction entities. To attain this end, this research conducts two research thrusts: (i) object localization using a deep neural network, YOLO-V3; and (ii) development of an image rectification method that allows for the measurement of actual distance from a 2D image collected from a UAV. Tests on real-site aerial videos show the promising accuracy of the proposed method; the mean absolute distance errors for estimated proximity were less than 0.9 m and the mean absolute percentage errors were around 4%. The proposed method enables the advanced detection of struck-by hazards around workers, which in turn can make timely intervention possible. This proactive intervention can ultimately promote a safer working environment for construction workers.

Original languageEnglish (US)
Pages (from-to)168-182
Number of pages15
JournalAutomation in Construction
Volume99
DOIs
StatePublished - Mar 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

Keywords

  • Computer vision
  • Deep neural network
  • Proximity monitoring
  • Struck-by accident
  • Unmanned aerial vehicle

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