Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams
DC Field | Value | Language |
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dc.contributor.author | Kim, H. | - |
dc.contributor.author | Lee, W. | - |
dc.contributor.author | Kim, M. | - |
dc.contributor.author | Moon, Y. | - |
dc.contributor.author | Lee, T. | - |
dc.contributor.author | Cho, M. | - |
dc.contributor.author | Mun, D. | - |
dc.date.accessioned | 2021-12-01T10:41:46Z | - |
dc.date.available | 2021-12-01T10:41:46Z | - |
dc.date.created | 2021-08-31 | - |
dc.date.issued | 2021-11-30 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/128554 | - |
dc.description.abstract | Piping and instrumentation diagrams (P&IDs) are commonly used in the process industry as a transfer medium for the fundamental design of a plant and for detailed design, purchasing, procurement, construction, and commissioning decisions. The present study proposes a method for symbol and text recognition for P&ID images using deep-learning technology. Our proposed method consists of P&ID image pre-processing, symbol and text recognition, and the storage of the recognition results. We consider the recognition of symbols of different sizes and shape complexities in high-density P&ID images in a manner that is applicable to the process industry. We also standardize the training dataset structure and symbol taxonomy to optimize the developed deep neural network. A training dataset is created based on diagrams provided by a local Korean company. After training the model with this dataset, a recognition test produced relatively good results, with a precision and recall of 0.9718 and 0.9827 for symbols and 0.9386 and 0.9175 for text, respectively. © 2021 Elsevier Ltd | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mun, D. | - |
dc.identifier.doi | 10.1016/j.eswa.2021.115337 | - |
dc.identifier.scopusid | 2-s2.0-85107856404 | - |
dc.identifier.wosid | 000694989100003 | - |
dc.identifier.bibliographicCitation | Expert Systems with Applications, v.183 | - |
dc.relation.isPartOf | Expert Systems with Applications | - |
dc.citation.title | Expert Systems with Applications | - |
dc.citation.volume | 183 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | High density | - |
dc.subject.keywordAuthor | Object recognition | - |
dc.subject.keywordAuthor | Piping and instrumentation diagrams | - |
dc.subject.keywordAuthor | Symbols | - |
dc.subject.keywordAuthor | Texts | - |
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