Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks.

Moreno-García, Carlos Francisco; Johnston, Pam; Garkuwa, Bello

Abstract

One of the key features of most document image digitisation systems is the capability of discerning between the main components of the printed representation at hand. In the case of engineering drawings, such as circuit diagrams, telephone exchanges or process diagrams, the three main shapes to be localised are the symbols, text and connectors. While most of the state of the art devotes to top-down recognition approaches which attempt to recognise these shapes based on their features and attributes, less work has been devoted to localising the actual pixels that constitute each shape, mostly because of the difficulty in obtaining a reliable source of training samples to classify each pixel individually. In this work, we present a convolutional neural network (CNN) capable of classifying each pixel, using a type of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID) as a case study. To obtain the training patches, we have used a semi-automated heuristics-based tool which is capable of accurately detecting and producing the symbol, text and connector layers of a particular P&ID standard in a considerable amount of time (given the need of human interaction). Experimental validation shows that the CNN is capable of obtaining these three layers in a reduced time, with the pixel window size used to generate the training samples having a strong influence on the recognition rate achieved for the different shapes. Furthermore, we compare the average run time that both the heuristics-tool and the CNN need in order to produce the three layers for a single diagram, indicating future directions to increase accuracy for the CNN without compromising the speed.

Citation

MORENO-GARCÍA, C.F., JOHNSTON, P. and GARKUWA, B. 2020. Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207479. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207479

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congre
• Start Date: Jul 19, 2020
• End Date: Jul 24, 2020
• Acceptance Date: Mar 1, 2020
• Online Publication Date: Jul 19, 2020
• Publication Date: Sep 28, 2020
• Deposit Date: Oct 3, 2020
• Publicly Available Date: Oct 5, 2020
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Series ISSN: 2161-4407
• Book Title: Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with t
• ISBN: 9781728169262
• DOI: https://doi.org/10.1109/IJCNN48605.2020.9207479
• Keywords: Convolutional neural networks; Piping and instrumentation diagram; Pixel classification; Engineering drawing digitisation
• Public URL: https://rgu-repository.worktribe.com/output/972757