Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization

As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&ID. The proposed method consists of three s...

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Autores principales:	Yoochan Moon, Jinwon Lee, Duhwan Mun, Seungeun Lim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning image processing line object object recognition piping and instrumentation diagram Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7a85631caa734c8abb8c083696509c04
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id	oai:doaj.org-article:7a85631caa734c8abb8c083696509c04
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spelling	oai:doaj.org-article:7a85631caa734c8abb8c083696509c042021-11-11T15:08:17ZDeep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization10.3390/app1121100542076-3417https://doaj.org/article/7a85631caa734c8abb8c083696509c042021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10054https://doaj.org/toc/2076-3417As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&ID. The proposed method consists of three steps. In the first step of preprocessing, the outer border and title box in the diagram are removed. In the second step of detection, continuous lines are detected, and then line signs and flow arrows indicating the flow direction are detected. In the third step of post-processing, using the results of line sign detection, continuous lines that require changing of the line type are determined, and the line types are adjusted accordingly. Then, the recognized lines are merged with flow arrows. For verification of the proposed method, a prototype system was used to conduct an experiment of line recognition. For the nine test P&IDs, the average precision and recall were 96.14% and 89.59%, respectively, showing high recognition performance.Yoochan MoonJinwon LeeDuhwan MunSeungeun LimMDPI AGarticledeep learningimage processingline objectobject recognitionpiping and instrumentation diagramTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10054, p 10054 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning image processing line object object recognition piping and instrumentation diagram Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	deep learning image processing line object object recognition piping and instrumentation diagram Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yoochan Moon Jinwon Lee Duhwan Mun Seungeun Lim Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization
description	As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&ID. The proposed method consists of three steps. In the first step of preprocessing, the outer border and title box in the diagram are removed. In the second step of detection, continuous lines are detected, and then line signs and flow arrows indicating the flow direction are detected. In the third step of post-processing, using the results of line sign detection, continuous lines that require changing of the line type are determined, and the line types are adjusted accordingly. Then, the recognized lines are merged with flow arrows. For verification of the proposed method, a prototype system was used to conduct an experiment of line recognition. For the nine test P&IDs, the average precision and recall were 96.14% and 89.59%, respectively, showing high recognition performance.
format	article
author	Yoochan Moon Jinwon Lee Duhwan Mun Seungeun Lim
author_facet	Yoochan Moon Jinwon Lee Duhwan Mun Seungeun Lim
author_sort	Yoochan Moon
title	Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization
title_short	Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization
title_full	Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization
title_fullStr	Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization
title_full_unstemmed	Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization
title_sort	deep learning-based method to recognize line objects and flow arrows from image-format piping and instrumentation diagrams for digitization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7a85631caa734c8abb8c083696509c04
work_keys_str_mv	AT yoochanmoon deeplearningbasedmethodtorecognizelineobjectsandflowarrowsfromimageformatpipingandinstrumentationdiagramsfordigitization AT jinwonlee deeplearningbasedmethodtorecognizelineobjectsandflowarrowsfromimageformatpipingandinstrumentationdiagramsfordigitization AT duhwanmun deeplearningbasedmethodtorecognizelineobjectsandflowarrowsfromimageformatpipingandinstrumentationdiagramsfordigitization AT seungeunlim deeplearningbasedmethodtorecognizelineobjectsandflowarrowsfromimageformatpipingandinstrumentationdiagramsfordigitization
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Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization

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