합성 데이터와 텍스트-심볼 통합 검출을 활용한 이미지 형식 P&ID 인식 기법
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이원용 | - |
dc.contributor.author | 김미주 | - |
dc.contributor.author | 문두환 | - |
dc.contributor.author | 김형기 | - |
dc.date.accessioned | 2022-03-05T20:40:55Z | - |
dc.date.available | 2022-03-05T20:40:55Z | - |
dc.date.created | 2022-02-15 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2508-4003 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137905 | - |
dc.description.abstract | A Piping and Instrumentation Diagram (P&ID) is a diagram used in the process plant industry. Digital format P&ID like intelligent P&ID can utilize DB technology, so it is easy to search and modify. Therefore, its use in the field has become common. However, there are cases in which digital P&IDs do not exist but exist only in image format because they were created before the digital P&ID was universalized or for security reasons. Thus, a technique for converting image format P&ID to digital P&ID is required. In this study, by modifying the deep learning-based symbol and text recognition structure presented in previous studies for symbol and text recognition of image format P&ID we propose a new structure that can improve performance while reducing the amount of computation required for recognition. In addition, we propose a synthetic data generation method suitable for P&ID in order to improve symbol recognition performance through data augmentation of the P&ID dataset. An experiment was performed to confirm the symbol and text recognition performance through a total of 82 P&ID drawings, and it was confirmed that the symbol and text recognition performance was improved through the method proposed in this study. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국CDE학회 | - |
dc.title | 합성 데이터와 텍스트-심볼 통합 검출을 활용한 이미지 형식 P&ID 인식 기법 | - |
dc.title.alternative | Image Format P&ID Recognition Technique Using Synthetic Data and Text-symbol Integrated Detection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 문두환 | - |
dc.identifier.bibliographicCitation | 한국CDE학회 논문집, v.26, no.4, pp.355 - 365 | - |
dc.relation.isPartOf | 한국CDE학회 논문집 | - |
dc.citation.title | 한국CDE학회 논문집 | - |
dc.citation.volume | 26 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 355 | - |
dc.citation.endPage | 365 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002781556 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Piping and instrumentation diagrams | - |
dc.subject.keywordAuthor | Symbol detection | - |
dc.subject.keywordAuthor | Synthetic data | - |
dc.subject.keywordAuthor | Text detection | - |
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