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Multi-representation thermal features for enhanced defect analysis in pulse thermography.

Mohammed Salah1, Naoufel Werghi2, Davor Svetinovic2,3

  • 1Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

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|March 11, 2026
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Summary
This summary is machine-generated.

AI-driven pulse thermography (PT) enhances non-destructive testing by fusing Principal Component Thermography (PCT) and Thermographic Signal Reconstruction (TSR) features. This novel approach improves subsurface defect detection and depth estimation in industrial components.

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Non-Destructive Testing

Background:

  • AI-driven pulse thermography (PT) is vital for non-destructive testing (NDT) of industrial components.
  • Current methods often rely on Principal Component Thermography (PCT) or Thermographic Signal Reconstruction (TSR) independently, limiting performance.
  • These representations contain complementary semantic features crucial for accurate defect analysis.

Purpose of the Study:

  • To introduce PT-Fusion, a novel feature fusion network for enhanced subsurface defect analysis in PT.
  • To address the limitations of using PCT and TSR representations separately.
  • To improve both defect segmentation and depth estimation in PT inspections.

Main Methods:

  • Developed PT-Fusion, a network incorporating Adaptive Weighing Fusion Gate (AWFG) and Gating Enhanced Decoding Block (GEDB) modules.
  • Implemented adaptive fusion of thermal features from PCT and TSR representations.
  • Introduced a novel data augmentation technique using random sampling from thermographic sequences to overcome data scarcity.

Main Results:

  • PT-Fusion demonstrated superior performance over U-Net, attention U-Net, and 3D-CNN, achieving a 10% margin in defect segmentation and depth estimation accuracy.
  • PT-Fusion's performance was comparable to advanced models like TransUNet and Swin-UNet.
  • PT-Fusion achieved comparable results with a significantly lower number of parameters than TransUNet and Swin-UNet.

Conclusions:

  • PT-Fusion effectively fuses complementary thermal features from PCT and TSR for improved PT analysis.
  • The proposed network offers a more efficient and accurate solution for subsurface defect detection and depth estimation.
  • PT-Fusion represents a significant advancement in AI-driven non-destructive testing applications.