Article
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Towards Automated Measurement of As-Built Components Using Computer Vision
Version 1
: Received: 28 July 2023 / Approved: 31 July 2023 / Online: 1 August 2023 (10:20:49 CEST)
A peer-reviewed article of this Preprint also exists.
Perez, H.; Tah, J.H.M. Towards Automated Measurement of As-Built Components Using Computer Vision. Sensors 2023, 23, 7110. Perez, H.; Tah, J.H.M. Towards Automated Measurement of As-Built Components Using Computer Vision. Sensors 2023, 23, 7110.
Abstract
Regular inspections during construction work verify that the work completed is consistent with the plans and specifications and ensure that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision-based (CV) techniques for the measurement of as-built elements, predominantly use 3D laser scanning or 3D Photogrammetry modelling to determine the geometrical properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates making the deployment of these techniques on dynamic construction project sites a challenging task. In this paper, we propose a pipeline for automating the measurement of as-built components using artificial intelligence (AI) and computer vision (CV) techniques. The pipeline requires a single image obtained with a stereo-camera system to measure the size of selected objects or as-built components. We demonstrate our approach by measuring the size of concrete walls and columns. The novelty of this work is attributed to the fully automated CV-based method for measuring any given element using a single image only. The proposed solution is suitable for use in measuring the sizes of as-built components of built assets. It has the potential to be further developed and integrated with BIM models for use on construction projects for progress monitoring.
Keywords
Machine Learning; Computer Vision; Automated measurement
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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