Hybrid approach for comprehensive recognition of line objects contained in high-density piping and instrumentation diagrams using deep learning and rules

초록

This study presents a hybrid approach for the automated recognition and classification of line objects in piping and instrumentation diagrams (P&IDs), with the goal of supporting the digital transformation of chemical process design and operation. By integrating Deep Learning (DL) techniques with rule-based methods, the proposed approach extracts flow and signal paths from legacy P&ID images, enabling applications such as process simulation, safety verification, and control logic validation. The approach consists of two stages. In the first stage, all line objects in a P&ID are detected and categorized into lines with special signs and continuous lines. A DL model identifies directional arrows and determines the overall flow structure. In the second stage, the continuous lines are further classified into dimension, extension, and leader lines using the rule-based algorithms, according to their functional characteristics. The method was tested on 30 P&ID sheets from Project A and two from Project B. Initially, the model trained on Project A data achieved precision and recall rates of 95.02% and 93.09%, respectively. On Project B, the performance dropped to 88.92% and 84.76% due to domain shift. After applying transfer learning using the four additional Project B sheets, the performance improved to 95.32% precision and 91.55% recall. These results demonstrate the potential of the proposed approach for accurate and scalable conversion of P&ID data into structured formats, contributing to smart plant design and engineering data integration.

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