Automated Valve Detection in Piping and Instrumentation (P&ID) Diagrams

M. Gupta, C. Wei, T. Czerniawski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For successfully training neural networks, developers often require large and carefully labelled datasets. However, gathering such high-quality data is often time-consuming and prohibitively expensive. Thus, synthetic data are used for developing AI (Artificial Intelligence) /ML (Machine Learning) models because their generation is comparatively faster and inexpensive. The paper presents a proofof-concept for generating a synthetic labelled dataset for P&ID diagrams. This is accomplished by employing a data-augmentation approach of random cropping. The framework also facilitates the creation of a complete and automatically labelled dataset which can be used directly as an input to the deep learning models. We also investigate the importance of context in an image that is, the impact of relative resolution of a symbol and the background image. We have tested our algorithm for the symbol of a valve as a proof-of-concept and obtained encouraging results.

Original languageEnglish (US)
Title of host publicationProceedings of the 39th International Symposium on Automation and Robotics in Construction, ISARC 2022
PublisherInternational Association for Automation and Robotics in Construction (IAARC)
Pages630-637
Number of pages8
ISBN (Electronic)9789526952420
StatePublished - 2022
Event39th International Symposium on Automation and Robotics in Construction, ISARC 2022 - Bogota, Colombia
Duration: Jul 13 2022Jul 15 2022

Publication series

NameProceedings of the International Symposium on Automation and Robotics in Construction
Volume2022-July
ISSN (Electronic)2413-5844

Conference

Conference39th International Symposium on Automation and Robotics in Construction, ISARC 2022
Country/TerritoryColombia
CityBogota
Period7/13/227/15/22

Keywords

  • Convolution Neural Network
  • Deep Learning
  • Engineering Drawings
  • Piping and Instrumentation Drawings
  • Symbol Classification
  • Symbol Detection
  • Yolo

ASJC Scopus subject areas

  • Building and Construction
  • Civil and Structural Engineering
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence
  • Computer Science Applications

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