Deep learning for symbols detection and classification in engineering drawings.

Elyan, Eyad; Jamieson, Laura; Ali-Gombe, Adamu

Abstract

Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance. The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings.

Citation

ELYAN, E., JAMIESON, L. and ALI-GOMBE, A. 2020. Deep learning for symbols detection and classification in engineering drawings. Neural networks [online], 129, pages 91-102. Available from: https://doi.org/10.1016/j.neunet.2020.05.025

• Journal Article Type: Article
• Acceptance Date: May 19, 2020
• Online Publication Date: Jun 1, 2020
• Publication Date: Sep 30, 2020
• Deposit Date: Jun 8, 2020
• Publicly Available Date: Jun 2, 2021
• Journal: Neural networks
• Print ISSN: 0893-6080
• Electronic ISSN: 1879-2782
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 129
• Pages: 91-102
• DOI: https://doi.org/10.1016/j.neunet.2020.05.025
• Keywords: Deep learning; YOLO; P&ID; Engineering drawings; Symbols recognition; GANs
• Public URL: https://rgu-repository.worktribe.com/output/928703