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Abstract

Digitization of scanned piping and instrumentation diagrams (P&ID), widely used in manufacturing or mechanical industries such as oil and gas over several decades, has become a critical bottleneck in dynamic inventory management and creation of smart P&IDs that are compatible with the latest CAD tools. Historically, P&ID sheets have been manually generated at the design stage, before being scanned and stored as PDFs. Current digitization initiatives involve manual processing and are consequently very time-consuming, labor-intensive, and error-prone. Thanks to advances in image processing, and machine and deep learning techniques, there is an emerging body of work on P&ID digitization. However, existing solutions face several challenges owing to the variation in the scale, size, and noise in the P&IDs, the sheer complexity and crowdedness within the drawings, domain knowledge required to interpret the drawings, and the very minute visual differences among symbols. This motivates our current solution called Digitize-PID, which comprises an end-to-end pipeline for detection of core components from P&IDs such as pipes, symbols, and textual information, followed by their association with each other and, eventually, the validation and correction of output data based on inherent domain knowledge. A novel and efficient kernel-based line detection and a two-step method for detection of complex symbols based on a fine-grained deep recognition technique is presented in the paper. In addition, we have created an annotated synthetic dataset, Dataset-P&ID, of 500 P&IDs by incorporating different types of noise and complex symbols, which is made available for public use (currently there exists no public P&ID dataset). We evaluate our proposed method on this synthetic dataset and a real-world anonymized private dataset of 12 P&ID sheets. Results show that Digitize-PID outperforms the existing state-of-the-art for P&ID digitization.

Research area: Deep learning and AI

Authors: Shubham Singh Paliwal, Arushi Jain, Monika Sharma, Lovekesh Vig

Conference/event: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021)

Conference date: May 2021