LAURA JAMIESON l.jamieson4@rgu.ac.uk
Research Student
Few-shot symbol detection in engineering drawings.
Jamieson, Laura; Elyan, Eyad; Moreno-García, Carlos Francisco
Authors
Professor Eyad Elyan e.elyan@rgu.ac.uk
Professor
Dr Carlos Moreno-Garcia c.moreno-garcia@rgu.ac.uk
Associate Professor
Abstract
Recently, there has been significant interest in digitizing engineering drawings due to their complexity and practical benefits. Symbol digitization, a critical aspect in this field, is challenging as utilizing Deep Learning-based methods to recognize symbols of interest requires a large number of training instances for each class of symbols. Acquiring and annotating sufficient diagrams is difficult due to concerns about confidentiality and availability. The conventional manual annotation process is time-consuming, costly, and prone to human error. Additionally, obtaining an adequate number of samples for rare classes proves to be exceptionally challenging. This paper introduces a few-shot framework to address these challenges. Several experiments with fewer than ten, and sometimes just one, training instance per class using complex engineering drawings from industry sources were carried out. The results suggest that our method not only significantly improves symbol detection performance compared to other state-of-the-art methods but also decreases the necessary number of training instances.
Citation
JAMIESON, L., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Few-shot symbol detection in engineering drawings. Applied artificial intelligence [online], 38(1), article number e2406712. Available from: https://doi.org/10.1080/08839514.2024.2406712
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 11, 2024 |
Online Publication Date | Sep 29, 2024 |
Publication Date | Dec 31, 2024 |
Deposit Date | Oct 3, 2024 |
Publicly Available Date | Oct 3, 2024 |
Journal | Applied artificial intelligence |
Print ISSN | 0883-9514 |
Electronic ISSN | 1087-6545 |
Publisher | Taylor and Francis |
Peer Reviewed | Peer Reviewed |
Volume | 38 |
Issue | 1 |
Article Number | e2406712 |
DOI | https://doi.org/10.1080/08839514.2024.2406712 |
Keywords | Engineering drawings; Symbol digitization; Deep learning |
Public URL | https://rgu-repository.worktribe.com/output/2480905 |
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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