OSSR-PID: One-Shot Symbol Recognition in P&amp;ID Sheets using Path Sampling and GCN

Paliwal, Shubham; Sharma, Monika; Vig, Lovekesh

doi:10.1109/ijcnn52387.2021.9534122

Cited by 12 publications

(25 citation statements)

References 19 publications

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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). For example, Elyan et al (2020a) presented a You Only Look Once (YOLO) v3 (Redmon and Farhadi 2018) based detection method for symbols in real-world P&IDs.…”

Section: Application Domainsmentioning

confidence: 99%

“…A Generative Adversarial Network (GAN) based (Ali-Gombe and Elyan 2019) approach was used to synthesise more data to improve classification. Meanwhile, Paliwal et al (2021b) used a graph-based approach for symbol recognition. Other studies focussed on the text (Jamieson et al 2020;Francois et al 2022) or connectors (Moon et al 2021).…”

Section: Application Domainsmentioning

confidence: 99%

“…These diagrams are prevalent across multiple industries, including electrical (De et al 2011), oil and gas (Elyan et al 2020a), and architecture (Kim et al 2021a). Manual analysis of these diagrams is time-consuming, prone to human error (Paliwal et al 2021a, b) and requires subject matter experts (Paliwal et al 2021a). There has recently been an increasing demand to digitise these diagrams for use in processes including asset performance management (Mani et al 2020), safety studies (Gao et al 2020), and data analytics (Moreno-García et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the recent and unprecedented progress, digitising engineering drawings continues to be a challenging problem (Moreno-García et al 2018). First of all, these diagrams are very complex, containing a large number of similar (Paliwal et al 2021a;Rahul et al 2019) and overlapping (Rahul et al 2019) shapes. For example, Elyan et al (2020a) reported on average 180 symbols of different types in a real-world P&ID dataset.…”

Section: Introductionmentioning

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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Section: Application Domainsmentioning

confidence: 99%

Section: Application Domainsmentioning

confidence: 99%

Section: Application Domainsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection

Jamieson,

Moreno-Garcia,

Elyan

2024

IJDAR

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Construction drawings are frequently stored in undigitised formats and consequently, their analysis requires substantial manual effort. This is true for many crucial tasks, including material takeoff where the purpose is to obtain a list of the equipment and respective amounts required for a project. Engineering drawing digitisation has recently attracted increased attention, however construction drawings have received considerably less interest compared to other types. To address these issues, this paper presents a novel framework for the automatic processing of construction drawings. Extensive experiments were performed using two state-of-the-art deep learning models for object detection in challenging high-resolution drawings sourced from industry. The results show a significant reduction in the time required for drawing analysis. Promising performance was achieved for symbol detection across various classes, with a mean average precision of 79% for the YOLO-based method and 83% for the Faster R-CNN-based method. This framework enables the digital transformation of construction drawings, improving tasks such as material takeoff and many others.

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Extraction of line objects from piping and instrumentation diagrams using an improved continuous line detection algorithm

et al. 2023

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OSSR-PID: One-Shot Symbol Recognition in P&ID Sheets using Path Sampling and GCN

Cited by 12 publications

References 19 publications

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

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

Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection

Extraction of line objects from piping and instrumentation diagrams using an improved continuous line detection algorithm

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