Computer Vision Applications In Construction And Asset Management Phases: A Literature Review

Abstract

Recent advances in digital photography and unmanned aerial vehicle (UAV) platforms make visual data from construction project sites more accessible to project teams. To semi-automatically or automatically obtain the essential information, evaluate the ongoing activities or operations, and address project-level challenges, researchers have focused on applying various computer vision (CV)-based methods to process and interpret the acquired visual data. This research developed a framework to summarize the vision-based methods that have been applied to construction/asset management operations through a systematic literature review. The reviewed literature was composed of 103 journal papers from 2011 to 2020. All the reviewed journal papers were from the Ei Compendex database with specific search criteria. The developed framework consisted of two parts: use cases and CV domains. Use cases contained five aspects: safety monitoring, productivity improvement, progress monitoring, infrastructure inspection, and robotic application. CV domains contained six aspects: image processing, object classification, object detection, object tracking, pose estimation, and 3D reconstruction. All eleven aspects were integrated from the reviewed papers. For each reviewed paper, the general workflow of applied vision-based approaches was described and categorized into each use case. A trending timeline was developed to analyze the popularity of the identified use cases and CV domains within the reviewed time period. Both the quantity and variety of construction use cases and CV domains have increased. Challenges and limitations of applying CV-based methods in the construction industry were also identified. This paper provides readers with a summary of how CV-based methods have been used in the construction industry and serve as a reference for future research and development.