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<p>Numerous studies have focused on digitizing piping and instrumentation diagrams (P&amp;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&amp;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-based feature vector similarity calculation, text error detection based on distance-based detection, and line error detection through intersection-case inspection method. The proposed method was validated through experiments conducted using P&amp;IDs from actual industrial sites. The results showed that unrecognized object inspection achieved a recall of 100%. Misrecognized object inspection achieved an accuracy of 99.2% and an F1 score of 96.7% for symbols, an accuracy of 95.8% and an F1 score of 97.3% for text, and 100% accuracy and F1 score for lines. Overall, error correction time was reduced by approximately 40%.</p>

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Automated inspection of P&ID object recognition using deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

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  • Ji-Beob Kim,
  • Yoochan Moon,
  • Seung-Tae Han,
  • Duhwan Mun

摘要

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-based feature vector similarity calculation, text error detection based on distance-based detection, and line error detection through intersection-case inspection method. The proposed method was validated through experiments conducted using P&IDs from actual industrial sites. The results showed that unrecognized object inspection achieved a recall of 100%. Misrecognized object inspection achieved an accuracy of 99.2% and an F1 score of 96.7% for symbols, an accuracy of 95.8% and an F1 score of 97.3% for text, and 100% accuracy and F1 score for lines. Overall, error correction time was reduced by approximately 40%.