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Article

Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network

1
Plant Engineering Center, Institute for Advanced Engineering, Yongin-si 17180, Korea
2
Department of Precision Mechanical Engineering, Kyungpook National University, Sangju-si 37224, Korea
*
Author to whom correspondence should be addressed.
Energies 2019, 12(23), 4425; https://doi.org/10.3390/en12234425
Received: 8 October 2019 / Revised: 16 November 2019 / Accepted: 19 November 2019 / Published: 21 November 2019
A piping and instrumentation diagram (P&ID) is a key drawing widely used in the energy industry. In a digital P&ID, all included objects are classified and made amenable to computerized data management. However, despite being widespread, a large number of P&IDs in the image format still in use throughout the process (plant design, procurement, construction, and commissioning) are hampered by difficulties associated with contractual relationships and software systems. In this study, we propose a method that uses deep learning techniques to recognize and extract important information from the objects in the image-format P&IDs. We define the training data structure required for developing a deep learning model for the P&ID recognition. The proposed method consists of preprocessing and recognition stages. In the preprocessing stage, diagram alignment, outer border removal, and title box removal are performed. In the recognition stage, symbols, characters, lines, and tables are detected. The objects for recognition are symbols, characters, lines, and tables in P&ID drawings. A new deep learning model for symbol detection is defined using AlexNet. We also employ the connectionist text proposal network (CTPN) for character detection, and traditional image processing techniques for P&ID line and table detection. In the experiments where two test P&IDs were recognized according to the proposed method, recognition accuracies for symbol, characters, and lines were found to be 91.6%, 83.1%, and 90.6% on average, respectively. View Full-Text
Keywords: deep learning; piping and instrumentation diagram; object recognition deep learning; piping and instrumentation diagram; object recognition
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MDPI and ACS Style

Yu, E.-S.; Cha, J.-M.; Lee, T.; Kim, J.; Mun, D. Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network. Energies 2019, 12, 4425. https://doi.org/10.3390/en12234425

AMA Style

Yu E-S, Cha J-M, Lee T, Kim J, Mun D. Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network. Energies. 2019; 12(23):4425. https://doi.org/10.3390/en12234425

Chicago/Turabian Style

Yu, Eun-Seop, Jae-Min Cha, Taekyong Lee, Jinil Kim, and Duhwan Mun. 2019. "Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network" Energies 12, no. 23: 4425. https://doi.org/10.3390/en12234425

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