Enabling Component-Based Progress Monitoring on Construction Sites Through Image-Based Computer Vision
Year: 2024
Type: conference-paper
Abstract: 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.
DOI: 10.15480/882.13520
Authors: Friedl, Felix, Pfitzner, Fabian
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6944022254
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