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Symbols classification in engineering drawings.

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

Authors

Chrisina Jayne



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.

Start Date Jul 8, 2018
Publication Date Dec 31, 2018
Print ISSN 2161-4393
Electronic ISSN 2161-4407
Publisher Institute of Electrical and Electronics Engineers
Article Number 8489087
Series ISSN 2161-4407
Institution 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
DOI https://doi.org/10.1109/IJCNN.2018.8489087
Keywords Technical drawings; Digitisation; Symbols; Engineering

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