Automatic Assessment for Engineering Drawing

Goh, Kim Nee; Shukri, Siti Rohkmah Mohd; Manao, Rofans Beleam

doi:10.1007/978-3-319-02958-0_45

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…There exists very limited work on digitizing the content of engineering diagrams to facilitate fast and efficient extraction of information. The authors (Goh et al, 2013) automated the assessment of AutoCAD Drawing Exchange Format (DXF) by converting DXF file into SVG format and developing a marking algorithm of the generated SVG files. A framework for engineering drawings recognition using a case-based approach is proposed by (Yan and Wenyin, 2003) where the user interactively provides an example of one type of graphic object in an engineering drawing and then system tries to learn the graphical knowledge of this type of graphic object from the example and later use this learned knowledge to recognize or search for similar graphic objects in engineering drawings.…”

Section: Related Workmentioning

confidence: 99%

Automatic Information Extraction from Piping and Instrumentation Diagrams

Rahul

Paliwal

Sharma

et al. 2019

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods

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One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. These files need to be digitized for purposes of inventory management and updation, and easy reference to different components of the schematics. There are several challenging vision problems associated with digitizing real world P&ID diagrams. Real world P&IDs come in several different resolutions, and often contain noisy textual information. Extraction of instrumentation information from these diagrams involves accurate detection of symbols that frequently have minute visual differences between them. Identification of pipelines that may converge and diverge at different points in the image is a further cause for concern. Due to these reasons, to the best of our knowledge, no system has been proposed for end-to-end data extraction from P&ID diagrams. However, with the advent of deep learning and the spectacular successes it has achieved in vision, we hypothesized that it is now possible to re-examine this problem armed with the latest deep learning models.To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols. This is followed by association of the detected components with the appropriate pipeline. The extracted pipeline information is used to populate a tree-like data structure for capturing the structure of the piping schematics. We have also evaluated our proposed method on a real world dataset of P&ID sheets obtained from an oil firm and have obtained extremely promising results. To the best of our knowledge, this is the first system that performs end-to-end data extraction from P&ID diagrams.

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Section: Related Workmentioning

confidence: 99%

Automatic Information Extraction from Piping and Instrumentation Diagrams

Rahul

Paliwal

Sharma

et al. 2019

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods

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“…Future developments of the project are addressed to define test assessment, and content validity and results validation. Other interesting and innovative ideas consider the automated correction of CAD drawings and others related to the use of a web‐based test for the verification of design knowledge [11,16]. Some other assessment information may be extracted by the analysis of TD or engineering graphics courses’ syllabus, from engineering courses curriculum, or Ministerial programs.…”

Section: Trends and Innovations In Teaching Td And/or Engineering Gramentioning

confidence: 99%

TDEG based framework and tools for innovation in teaching technical drawing: The example of LaMoo project

Villa

Motyl

Paderno

et al. 2018

Comp Applic In Engineering

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This work presents the development of an integrated framework and related tools for innovation and improvement in the teaching of Technical Drawing. This framework is based on the “Technical Drawing Evaluation Grid—TDEG.” This grid is currently used, by the authors, for the definition and the development of different kinds of tools for supporting both teaching and learning and for the evaluation of Technical Drawing and engineering graphics topics knowledge in general. In particular, this paper focuses on the problems related to knowledge evaluation and assessment of Technical Drawing using online tests. Then, the LaMoo project which is a tool under development for the structured construction of questions for online tests in Moodle environment is presented.

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“…Floor Plan diagrams [1]), Oil and Gas (i.e. P&IDs [2]), Mechanical Engineering [3], AutoCAD Drawing Exchange Format (DXF) [4] and others. Interpreting these drawings requires highly skilled people, and in some cases long hours of work.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for symbols detection and classification in engineering drawings

2020

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Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance.The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings.

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Automatic Assessment for Engineering Drawing

Cited by 9 publications

References 6 publications

Automatic Information Extraction from Piping and Instrumentation Diagrams

Automatic Information Extraction from Piping and Instrumentation Diagrams

TDEG based framework and tools for innovation in teaching technical drawing: The example of LaMoo project

Deep learning for symbols detection and classification in engineering drawings

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