Symbols classification in engineering drawings.

Elyan, Eyad; Garcia, Carlos Moreno; Jayne, Chrisina

Abstract

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we present a semi-automatic and heuristic-based approach to detect and localise symbols within these drawings. This includes generating a labeled dataset from real world engineering drawings and investigating the classification performance of three different state-of the art supervised machine learning algorithms. In order to improve the classification accuracy the dataset was pre-processed using unsupervised learning algorithms to identify hidden patterns within classes. Testing and evaluating the proposed methods on a dataset of symbols representing one standard of drawings, namely Process and Instrumentation (P&ID) showed very competitive results.

Citation

ELYAN, E., MORENO GARCIA, C. and JAYNE, C. 2018. Symbols classification in engineering drawings. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489087. Available from: https://doi.org/10.1109/IJCNN.2018.8489087

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2018 International joint conference on neural networks (IJCNN 2018)
• Start Date: Jul 8, 2018
• End Date: Jul 13, 2018
• Acceptance Date: Mar 15, 2018
• Online Publication Date: Jul 8, 2018
• Publication Date: Dec 31, 2018
• Deposit Date: Apr 17, 2018
• Publicly Available Date: Jul 8, 2018
• Print ISSN: 2161-4393
• Electronic ISSN: 2161-4407
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 8489087
• Series ISSN: 2161-4407
• DOI: https://doi.org/10.1109/IJCNN.2018.8489087
• Keywords: Technical drawings; Digitisation; Symbols; Engineering
• Public URL: http://hdl.handle.net/10059/2873
• Contract Date: Apr 17, 2018