New trends on digitisation of complex engineering drawings.

Abstract

Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in Deep Neural Networks, automatic analysis and processing of these engineer- ing drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping & instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions.

Citation

MORENO-GARCIA, C.F., ELYAN, E. and JAYNE, C. 2019. New trends on digitisation of complex engineering drawings. Neural computing and applications [online], 31(6): selected papers from the proceedings of the 18th Engineering applications of neural networks conference (EANN 2017), 25-27 August 2017, Athens, Greece, pages 1695-1712. Available from: https://doi.org/10.1007/s00521-018-3583-1

• Journal Article Type: Conference Paper
• Conference Name: 18th Engineering applications of neural networks conference (EANN 2017)
• Conference Location: Athens, Greece
• Acceptance Date: Jun 4, 2018
• Online Publication Date: Jun 13, 2018
• Publication Date: Jun 30, 2019
• Deposit Date: Jun 7, 2018
• Publicly Available Date: Jun 7, 2018
• Journal: Neural computing and applications
• Print ISSN: 0941-0643
• Electronic ISSN: 1433-3058
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Issue: 6
• Pages: 1695-1712
• DOI: https://doi.org/10.1007/s00521-018-3583-1
• Keywords: Engineering drawing; Digitisation; Contextualisation; Segmentation; Feature extraction; Recognition; Classification; Deep learning; Convolutional neural networks
• Public URL: http://hdl.handle.net/10059/2949