Few-Shot Symbol Detection in Engineering Drawings

• Published in: Applied artificial intelligence Vol. 38; no. 1
• Main Authors: Jamieson, Laura, Elyan, Eyad, Moreno-García, Carlos Francisco
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 31.12.2024
• ISSN: 0883-9514; 1087-6545
• DOI: 10.1080/08839514.2024.2406712

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.