End-to-end digitization of image format piping and instrumentation diagrams at an industrially applicable level

نویسندگان

چکیده

Abstract This study proposes an end-to-end digitization method for converting piping and instrumentation diagrams (P&IDs) in the image format to digital P&IDs. Automating this process is important concern plant industry because presently P&IDs are manually converted into The proposed comprises object recognition within P&ID images, topology reconstruction of recognized objects, generation. A data set comprising 75 031 symbol, 10 073 text, 90 054 line was constructed train deep neural networks used recognizing symbols, lines. Topology generation were developed based on traditional rule-based approaches. Five test digitalized experiments. experimental results lines showed good precision recall performance, with averages 96.65%/96.40%, 90.65%/92.16%, 95.25%/87.91%, respectively. average 99.56% 96.07%. completed <3.5 hours (8488.2 s average) five

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ژورنال

عنوان ژورنال: Journal of Computational Design and Engineering

سال: 2022

ISSN: ['2288-5048', '2288-4300']

DOI: https://doi.org/10.1093/jcde/qwac056