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.
Highlights
Engineering diagrams (EDs) are schematic drawings describing process flow, circuit construction, and engineering device information
We propose a model based on an region-based convolutional neural network (R-convolutional neural network (CNN)) architecture that features dummy image clustering
We implemented a customized procedure for each target symbol integrated the proposed regions into one diagram
Summary
Engineering diagrams (EDs) are schematic drawings describing process flow, circuit construction, and engineering device information. Among the many types of EDs, piping and instrument diagrams (P&IDs) are broadly used in the production plant industry because they contain key information of the plant, including piping, valves, instruments, control logic, and annotations. Most of the plant industries, such as oil and gas production plants, have employed large teams of engineers to manually count these entities and digitalize the information into their internal systems because there is no module available to automatically extract such information from the diagrams. These tasks have been considered inefficient and time-consuming tasks. The demand for a module enabling an automatic engineering diagram digitalization has increased as such procedures can improve productivity and gain a competitive edge for the company in the global market
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