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

Eun-Seop Yu, Jae-Min Cha, Taekyong Lee, Jinil Kim and Duhwan Mun
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Eun-Seop Yu: Plant Engineering Center, Institute for Advanced Engineering, Yongin-si 17180, Korea
Jae-Min Cha: Plant Engineering Center, Institute for Advanced Engineering, Yongin-si 17180, Korea
Taekyong Lee: Plant Engineering Center, Institute for Advanced Engineering, Yongin-si 17180, Korea
Jinil Kim: Plant Engineering Center, Institute for Advanced Engineering, Yongin-si 17180, Korea
Duhwan Mun: Department of Precision Mechanical Engineering, Kyungpook National University, Sangju-si 37224, Korea

Energies, 2019, vol. 12, issue 23, 1-19

Abstract: 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.

Keywords: deep learning; piping and instrumentation diagram; object recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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