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

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 proof-of-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.