Towards a Robust Deep Learning-Based Scale Inference Approach in Construction Drawings
Publication: Computing in Civil Engineering 2023
ABSTRACT
Engineering drawings from various domains, for example, the construction industry, exhibit the geometry of the contained components in certain scales. The drawing’s real-world scale is not necessarily stored as meta information with the drawing file. This information is only implicitly contained by dimension lines. This paper presents a multistage pipeline to infer the true scale using deep learning-based methods and logical reasoning. By using the state-of-the-art object detection method YOLOv7 and the robust optical character recognition model EasyOCR, the proposed approach localizes each dimension line and interprets their respective length. The global scale of the drawing is determined by a voting scheme resulting in the most likely pixel-resolution. The method is tested on bridge construction drawings and shows promising results in all stages of the pipeline. The authors plan to publish the trained model weights together with the source code.
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Computing in Civil Engineering 2023
Pages: 721 - 728
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Published online: Jan 25, 2024
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