/Internal-Pipe-Corrosion-Assessment-with-Ultrasound-and-CNNs

This study introduces a dual-mode methodology for quantifying pipe corrosion by employing ultrasound technology in conjunction with convolutional neural networks (CNN).

Primary LanguagePython

Internal Pipe Corrosion Assessment with Ultrasound and CNNs

This study introduces a dual-mode methodology for quantifying pipe corrosion by employing ultrasound technology in conjunction with convolutional neural networks (CNN). The codes include components for data handling, model development, preprocessing, and utility functions.

Components

  • Data:

    • data/: Directory containing the original ultrasound data.

  • Data Handling:

    • data.py: Functions for loading datasets.

  • Model Development:

    • model/: Directory containing various CNN architectures:
      • AlexNet.py
      • DenseNet.py
      • EfficientNet.py
      • InceptionNet.py
      • ResNet.py
      • VGG.py
      • __pycache__/: Cached Python bytecode files.

  • Preprocessing:

    • preprocess.py: Implementation code for data preprocessing.
    • preprocess_fn.py: Functions required for preprocessing.

  • Execution Scripts:

    • run_experiment.py: Main execution script.
    • training.py: Contains classes and functions related to training.
    • utils.py: Collection of utility functions.

  • Output and Results:

    • confusion_matrix.zip, output.hwp, plot.zip, vgg_loss_plot.zip: Output files and archives related to results and plots.
    • draw_acc-lossplot.py: Script for plotting accuracy and loss.

Usage

  • Run run_experiment.py to execute the main functionalities of the project.
  • The model/ directory contains implementations of different CNN architectures.

Experimental Results

Validation Accuracy and Loss

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Confusion Matrix

effcientNet

InceptionNet

VGGNet

ResNet