Dissertations and Theses
Date of Award
2025
Document Type
Thesis
Department
Computer Science
First Advisor
Jin Chen
Second Advisor
Zhigang Zhu
Keywords
BIM Reconstruction, Engineering Drawing, Image Processing, Computer Vision.
Abstract
Many legacy buildings lack Building Information Models (BIMs), even though they have detailed engineering drawings that contain essential mechanical, electrical, plumbing (MEP) components and structural components. Converting these drawings into BIM models remains a largely manual process, which is time-consuming, difficult to scale, and costly. This thesis presents a computer vision–based pipeline designed to automate two fundamental steps necessary for downstream BIM reconstruction from engineering drawings: legend extraction and engineering drawing preprocessing and noise removal. The legend extraction module combines histogram segmentation with Paddle OCR, enabling accurate extraction across heterogeneous MEP legend images. The engineering drawing preprocessing and noise removal module removes background structural components and employs a multi-stage approach for detecting and removing annotation arrows along with annotations, which commonly obscure MEP components. A sequence of morphological filtering and geometric analysis enables robust annotation arrows detection and removal, producing cleaner drawings for future analysis. Experimental results on engineering drawings of multiple real-world construction projects show strong legend segmentation accuracy, reliable annotation arrows detection, and consistent preprocessing performance across diverse engineering drawing layouts. The proposed pipeline establishes essential preprocessing and extraction capabilities that significantly reduce manual effort and provide a foundation for developing automated BIM models.
Recommended Citation
Chen, Suyang, "Engineering Drawing Segmentation and Understanding For Building Construction Digital Twins" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/cc_etds_theses/1240
