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Automatic Detection of Shaft Locations in Site Plans
Citation Link: https://doi.org/10.15480/882.13530
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
Ruhr University Bochum
Ruhr University Bochum
TORE-DOI
Start Page
317
End Page
324
Citation
35. Forum Bauinformatik, fbi 2024: 317-324
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
Site plans play a crucial role in construction projects, providing detailed layouts of structures and infrastructure components. Extracting specific information, such as sewage system details including shafts and pipeline routes, from these plans is essential for accurate cost estimation. However, it remains a labor-intensive task, especially for pixel-based drawings lacking machineinterpretable vector geometries and machine-readable text elements. In this study, we conceptualize an automated method to streamline this process, leveraging object detection and optical character recognition (OCR) techniques. The proposed approach involves three main steps: (1) locating the legend region in which the shaft symbol is specified using a specialized OCR method, (2) identifying the relevant shaft symbol from the plan’s legend, and (3) detecting shaft locations within site plans using state-of-the-art object detection algorithms. Developing this method aims to significantly increase efficiency in construction cost estimation by automating the tedious task of extracting and analyzing site plan data. The preliminary results included in this study demonstrate candidate techniques for symbol processing in site plans.
Subjects
automation
computer vision
deep learning
drawing analysis
site plan
DDC Class
624: Civil Engineering, Environmental Engineering
690: Building, Construction
006: Special computer methods
Publication version
publishedVersion
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Name
Automatic Detection of Shaft Locations in Site Plans.pdf
Type
Main Article
Size
1.61 MB
Format
Adobe PDF