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

Moon, Yoochan; Lee, Jinwon; Mun, Duhwan; Lim, Seungeun

doi:10.3390/app112110054

Cited by 18 publications

(14 citation statements)

References 38 publications

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“…However, the method is not directly applicable to the recognition of dashed lines. Similarly, Moon et al [19] introduced a method for recognizing lines and flow arrows in piping and instrumentation diagrams. The model was trained by a combination of image processing techniques and deep neural networks.…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches

Plumed,

Contero,

Naya

et al. 2024

Applied Sciences

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Sketched drawings sometimes include non-solid lines drawn as sets of consecutive strokes. They represent dashed lines, which are useful for various purposes. Recognizing such dashed lines while parsing drawings is reasonably straightforward if they are outlined with a ruler and compass but becomes challenging when they are hand-drawn. The problem is manageable if the strokes are drawn consecutively so we can leverage the entire sequence. However, it becomes more challenging if they are drawn unordered, and/or we do not have access to the sequence (like in batch vectorization). In this paper, we describe a new approach to identify groups of strokes as depicting single hand-drawn dashed lines. The approach does not use sequence information and is tolerant with irregularities and imprecisions of the strokes. Our goal is to identify hidden lines of sketched engineering line-drawings, which would enable the interpretation of line-drawings with hidden edges, which currently cannot be efficiently vectorized. We speculate that other fields like hand-drawn graph interpretation may also benefit from our approach.

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Section: Related Workmentioning

confidence: 99%

A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches

Plumed,

Contero,

Naya

et al. 2024

Applied Sciences

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“…The reviewed literature is listed by application and extracted data type in Table 1. Amongst these applications, there has been a considerable research focus on P&IDs (Rahul et al 2019;Sinha et al 2019;Yu et al 2019;Mani et al 2020;Gao et al 2020;Elyan et al 2020a;Moreno-García et al 2020;Jamieson et al 2020;Nurminen et al 2020;Paliwal et al 2021a;Moon et al 2021;Kim et al 2021b;Stinner et al 2021;Paliwal et al 2021b;Toral et al 2021;Bhanbhro et al 2022;Hantach et al 2021). Another research area is architecture diagram digitisation (Ziran and Marinai 2018;Zhao et al 2020;Rezvanifar et al 2020;Kim et al 2021a;Renton et al 2021;Jakubik et al 2022).…”

Section: Application Domainsmentioning

confidence: 99%

“…Most of the P&ID digitisation literature focussed on the extraction of specific data types (Sinha et al 2019;Gao et al 2020;Elyan et al 2020a;Jamieson et al 2020;Nurminen et al 2020;Moon et al 2021;Kim et al 2021b;Stinner et al 2021;Paliwal et al 2021b;Toral et al 2021). There is a particular focus on P&ID symbols (Elyan et al 2020a;Nurminen et al 2020;Paliwal et al 2021b).…”

Section: Application Domainsmentioning

confidence: 99%

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A review of deep learning methods for digitisation of complex documents and engineering diagrams

Jamieson,

Francisco Moreno-García,

Elyan

2024

Artif Intell Rev

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This paper presents a review of deep learning on engineering drawings and diagrams. These are typically complex diagrams, that contain a large number of different shapes, such as text annotations, symbols, and connectivity information (largely lines). Digitising these diagrams essentially means the automatic recognition of all these shapes. Initial digitisation methods were based on traditional approaches, which proved to be challenging as these methods rely heavily on hand-crafted features and heuristics. In the past five years, however, there has been a significant increase in the number of deep learning-based methods proposed for engineering diagram digitalisation. We present a comprehensive and critical evaluation of existing literature that has used deep learning-based methods to automatically process and analyse engineering drawings. Key aspects of the digitisation process such as symbol recognition, text extraction, and connectivity information detection, are presented and thoroughly discussed. The review is presented in the context of a wide range of applications across different industry sectors, such as Oil and Gas, Architectural, Mechanical sectors, amongst others. The paper also outlines several key challenges, namely the lack of datasets, data annotation, evaluation and class imbalance. Finally, the latest development in digitalising engineering drawings are summarised, conclusions are drawn, and future interesting research directions to accelerate research and development in this area are outlined.

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“…25 Although digitalization of engineering drawings with the help of AI still requires more attention in the research community, 26 several works on this subject have been recently published to help practitioners. [26][27][28][29][30][31] Since 2010, some researchers have begun to use neural networks and machine-learning algorithms for pattern recognition, recognition of text, lines, and symbols from scanned images or PDFs, and automatic generation of digitalized drawings. 32 The process involved identifying various text codes and symbols and localizing them in a very complex structure.…”

Section: Ai For Symbol Recognition and Digitalizing Engineering Drawingsmentioning

confidence: 99%

Using artificial intelligence to find design errors in the engineering drawings

Dzhusupova

Banotra²,

Bosch

et al. 2023

J Software Evolu Process

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Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule‐based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI‐based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost‐benefit analysis and potential scale‐up of the developed software. Our goal is to share the successful experience of AI‐based product development that can substantially reduce the engineering hours and, therefore, reduce the project's overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry.

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

Cited by 18 publications

References 38 publications

A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches

A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches

A review of deep learning methods for digitisation of complex documents and engineering diagrams

Using artificial intelligence to find design errors in the engineering drawings

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