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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information.

Kyle Dunphy1, Mohammad Navid Fekri2, Katarina Grolinger2

  • 1Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada.

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|August 26, 2022
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Summary
This summary is machine-generated.

Generative Adversarial Networks (GANs) can augment data for structural health monitoring (SHM). However, using GAN-generated synthetic data with convolutional neural networks (CNNs) for concrete damage detection slightly reduces classification accuracy compared to real data alone.

Keywords:
Generative Adversarial NetworksStructural Health Monitoringdamage detectiondata augmentationdeep learning

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Global infrastructure deterioration necessitates advanced Structural Health Monitoring (SHM) methods.
  • Traditional manual inspections for SHM are inefficient, subjective, and data-limited.
  • Deep Learning (DL) shows promise for automated SHM, but suffers from data scarcity.

Purpose of the Study:

  • To investigate the performance of DL-based multiclass damage identification using synthetic data generated by Generative Adversarial Networks (GANs).
  • To evaluate a convolutional neural network (CNN) architecture for concrete surface damage detection using GAN-generated synthetic images.
  • To quantify the correlation between classification accuracy and the quantity/diversity of synthetic data for data augmentation in SHM.

Main Methods:

  • Utilized Generative Adversarial Networks (GANs) to generate synthetic images of concrete surface damage.
  • Trained a convolutional neural network (CNN) architecture on datasets comprising real images, synthetic images, and hybrid combinations.
  • Quantified classification performance metrics (accuracy, validation, testing) for various training configurations.

Main Results:

  • CNN performance on hybrid datasets (real + synthetic) decreased by 10.6% (validation) and 7.4% (testing) compared to models trained solely on real data.
  • Models trained with both real and synthetic samples showed an average performance decrease of 1.6% compared to models trained only on real samples.
  • Increased synthetic data quantity led to decreased sample diversity and increased correlation between synthetic data and classification accuracy.

Conclusions:

  • While GANs can augment data for SHM, their use in multiclass damage identification with CNNs results in a slight decrease in classification performance.
  • The study highlights the trade-off between data quantity and diversity when using GANs for SHM data augmentation.
  • Further research is needed to optimize GAN architectures and training strategies for effective synthetic data generation in SHM applications.