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

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