Symbols are a universal way to convey complex information in technical drawings since they can represent a wide range of elements, including components, materials, or relationships, in a concise and space-saving manner. Therefore, to enable a digital and automatic interpretation of pixel-based drawings, accurate detection of symbols is a crucial step. To enhance the efficiency of the digitization process, current research focuses on automating this symbol detection using deep learning models. However, the ever-increasing repertoire of model architectures poses a challenge for researchers and practitioners alike in retaining an overview of the latest advancements and selecting the most suitable model architecture for their respective use cases. To provide guidance, this contribution conducts a comparative study of prevalent and state-of-the-art model architectures for the task of symbol detection in pixel-based construction drawings. Therefore, this study evaluates six different object detection model architectures, including YOLOv5, YOLOv7, YOLOv8, Swin-Transformer, ConvNeXt, and Faster-RCNN. These models are trained and tested on two distinct datasets from the bridge and residential building domains, both representing substantial sub-sectors of the construction industry. Furthermore, the models are evaluated based on five criteria, i.e., detection accuracy, robustness to data scarcity, training time, inference time, and model size. In summary, our comparative study highlights the performance and capabilities of different deep learning models for symbol detection in construction drawings. Through the comprehensive evaluation and practical insights, this research facilitates the advancement of automated symbol detection by showing the strengths and weaknesses of the model architectures, thus providing users with valuable guidance in choosing the most appropriate model for their real-world applications
Ruhr-University Bochum, Germany - ORCID: 0000-0003-1354-7817
Ruhr-University Bochum, Germany
Ruhr-University Bochum, Germany - ORCID: 0000-0002-2729-7743
Chapter Title
A Comparative Study of Deep Learning Models for Symbol Detection in Technical Drawings
Authors
Benedikt Faltin, Damaris Gann, Markus König
DOI
10.36253/979-12-215-0289-3.87
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Book Title
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Book Subtitle
Managing the Digital Transformation of Construction Industry
Editors
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/979-12-215-0289-3
eISBN (pdf)
979-12-215-0289-3
eISBN (xml)
979-12-215-0257-2
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846