Automated inspection of P&ID object recognition using deep learning.

ABSTRACT: Numerous studies have focused on digitizing piping and instrumentation diagrams (P&IDs) to enhance their applicability across industries. Even with the application of digitization technology, correcting errors in object recognition remains time-consuming, and because correction is performed manually, errors may persist. To address these issues, we propose a novel method for inspecting object recognition results in P&IDs. For unrecognized object inspection, patches are generated, and a deep learning-based classifier identifies missing elements. For misrecognized object inspection, optimal inspection methods are applied depending on the type of error, enabling effective detection of misrecognized objects. Specifically, the proposed misrecognition inspection methods include deep learning-base

SUBMITTER: Kim JB 

PROVIDER: S-EPMC12594774 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature