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Enabling Component-Based Progress Monitoring on Construction Sites Through Image-Based Computer Vision
Citation Link: https://doi.org/10.15480/882.13520
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
TORE-DOI
Start Page
349
End Page
356
Citation
35. Forum Bauinformatik, fbi 2024: 349-356
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
Vision-based construction monitoring methods have improved on-site transparency. However, many point cloud-based techniques are complex and often involve an image-dependent reconstruction step, making them prone to uncertainties. Additionally, few address productivity insights at the construction activity level. This paper presents a novel computer vision approach for automating construction progress monitoring, extracting information directly from image data enhanced through as-built details. A PIDNet Semantic segmentation model was trained to identify cast-in-place concrete walls, columns, and slabs during panel, rebar, and concrete phases. The detected components were processed using averaging techniques to monitor element-specific progress. The resulting data was integrated with as-built models through geometric projections, forming the basis for a digital twin construction. Our method was deployed on two-month construction data, providing detailed progress information and demonstrating its robustness. Compared to previous methods, this approach effectively merges existing as-built models with comprehensive as-performed image data.
Subjects
As-built geometry
Computer Vision
Construction Monitoring
Semantic Segmentation
DDC Class
624: Civil Engineering, Environmental Engineering
006: Special computer methods
620.3: Vibrations
Publication version
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Enabling Component-Based Progress Monitoring on Construction Sites Through Image-Based Computer Vision.pdf
Type
Main Article
Size
3.55 MB
Format
Adobe PDF