Deep Neural Network for Automatic Image Recognition of Engineering Diagrams
Dong-Yeol Yun, Seung-Kwon Seo, Umer Zahid, Chul‐Jin Lee
Abstract
Piping and instrument diagrams (P&IDs) are a key component of the process industry; they contain information about the plant, including the instruments, lines, valves, and control logic. However, the complexity of these diagrams makes it difficult to extract the information automatically. In this study, we implement an object-detection method to recognize graphical symbols in P&IDs. The framework consists of three parts—region proposal, data annotation, and classification. Sequential image processing is applied as the region proposal step for P&IDs. After getting the proposed regions, the unsupervised learning methods, k-means, and deep adaptive clustering are implemented to decompose the detected dummy symbols and assign negative classes for them. By training a convolutional network, it becomes possible to classify the proposed regions and extract the symbolic information. The results indicate that the proposed framework delivers a superior symbol-recognition performance through dummy detection.