합성 데이터와 텍스트-심볼 통합 검출을 활용한 이미지 형식 P&ID 인식 기법Image Format P&ID Recognition Technique Using Synthetic Data and Text-symbol Integrated Detection
- Other Titles
- Image Format P&ID Recognition Technique Using Synthetic Data and Text-symbol Integrated Detection
- Authors
- 이원용; 김미주; 문두환; 김형기
- Issue Date
- 2021
- Publisher
- 한국CDE학회
- Keywords
- Deep learning; Piping and instrumentation diagrams; Symbol detection; Synthetic data; Text detection
- Citation
- 한국CDE학회 논문집, v.26, no.4, pp.355 - 365
- Indexed
- KCI
- Journal Title
- 한국CDE학회 논문집
- Volume
- 26
- Number
- 4
- Start Page
- 355
- End Page
- 365
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/137905
- ISSN
- 2508-4003
- 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.
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Collections - College of Engineering > Department of Mechanical Engineering > 1. Journal Articles
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