Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks.

Elyan, Eyad; Moreno-García, Carlos Francisco; Johnston, Pamela

Abstract

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed.

Citation

ELYAN, E., MORENO-GARCÍA, C.F. and JOHNSTON, P. 2020. Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 215-224. Available from: https://doi.org/10.1007/978-3-030-48791-1_16

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 21st Engineering applications of neural networks conference 2020 (EANN 2020)
• Start Date: Jun 5, 2020
• End Date: Jun 7, 2020
• Acceptance Date: Mar 29, 2020
• Online Publication Date: May 28, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Jun 23, 2020
• Publicly Available Date: May 29, 2021
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 215-224
• Series Title: Proceedings of the International Neural Networks Society (INNS)
• Series Number: 2
• Series ISSN: 2661-8141
• Book Title: Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020
• ISBN: 9783030487904
• DOI: https://doi.org/10.1007/978-3-030-48791-1_16
• Keywords: Multiclass; Classification; Imbalanced dataset; Engineering drawings; P&ID
• Public URL: https://rgu-repository.worktribe.com/output/936537