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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deep learning for symbols detection and classification in engineering drawings.

Eyad Elyan1, Laura Jamieson1, Adamu Ali-Gombe1

  • 1School of Computing Science and Digital Media, Robert Gordon University, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|June 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces advanced methods for digitizing engineering drawings, improving symbol recognition and addressing data imbalance. The new techniques accurately identify over 94% of symbols, enhancing asset management and risk analysis.

Keywords:
Deep learningEngineering drawingsGANsP&IDSymbols recognitionYOLO

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Digitizing engineering drawings is crucial for industries like Oil and Gas and construction.
  • Analyzing these drawings is challenging due to symbol similarity and class imbalance.
  • Current methods struggle with accurate symbol localization and recognition in complex diagrams.

Purpose of the Study:

  • To develop an end-to-end bounding-box detection method for symbol localization and recognition.
  • To propose a Deep Generative Adversarial Neural Network (GAN) approach for handling class imbalance in symbol datasets.
  • To enhance the processing and analysis of engineering diagrams for improved business practices.

Main Methods:

  • An advanced, user-interaction-free bounding-box detection algorithm for symbol identification.
  • A Deep Generative Adversarial Neural Network (GAN) model designed to learn from limited training data.
  • Experimental validation using a large dataset of engineering diagrams from an industrial partner.

Main Results:

  • The bounding-box detection method achieved over 94% accuracy in symbol recognition.
  • The proposed GAN model effectively addressed the class-imbalance problem.
  • Significant improvements in the classification accuracy of engineering drawing symbols were demonstrated.

Conclusions:

  • The developed methods offer robust solutions for the challenges in processing engineering drawings.
  • Accurate symbol recognition and effective handling of class imbalance are achievable with the proposed techniques.
  • These advancements can significantly improve applications like inventory, asset management, and risk analysis.