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Few-shot symbol detection in engineering drawings.

Jamieson, Laura; Elyan, Eyad; Moreno-García, Carlos Francisco

Authors



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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JAMIESON 2024 Few-shot symbol detection (VOR) (3.4 Mb)
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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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