Comparing Traditional And Deep Learning Methods For Defect Detection In XCT Images.pdf

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Preview of Comparing Traditional and Deep Learning Methods for Defect Detection in XCT Images
🔗 Source: visielab.uantwerpen.be
📊 Size: 2.37 MB
👤 Author: Yosifov, Miroslav; Weinberger, Patrick; Reiter, Michael; Beenhouwer, Jan De; Sijbers, Jan; Kastner,
⬇️ Downloads: 31

Summary

- Conference: 12th Conference on Industrial Computed Tomography (iCT 2023), Fürth, Germany
- Authors: Miroslav Yosifov, Patrick Weinberger, Michael Reiter, Bernhard Fröhler, Jan De Beenhouwer, Jan Sijbers, Johann Kastner, Christoph Heinzl
- Objective: Compare traditional and deep learning methods for defect detection in X-ray computed tomography (XCT) images using probability of defect detection (POD).
- Methods:
- Traditional: k-means, watershed, Otsu thresholding
- Deep Learning: U-Net, V-Net, modified 3D U-Net (M-3DUnet)
- Data: Simulated XCT data from aluminum cylinder heads with varying defect sizes, shapes, and locations.
- Analysis:
- POD curves generated for six different defect types.
- Comparison of methods in 2D and 3D images, with POD curves and detection limits.
- Hyper-parameter optimization in deep learning methods to improve detection limits.
- Context: POD is increasingly used in XCT for process qualification, especially in fast in-line and at-line inspections. Traditional methods are still popular, but deep learning algorithms show promise in improving segmentation accuracy.

Description

Defect detectability analysis compares traditional and deep learning methods in numerical simulations for X-ray computed tomography (XCT) in non-destructive testing (NDT).

Technical Information

  • File Format: PDF
  • File Size: 2.37 MB
  • Pages: 10
  • Language: EN
  • Author: Yosifov, Miroslav; Weinberger, Patrick; Reiter, Michael; Beenhouwer, Jan De; Sijbers, Jan; Kastner,
  • Total Downloads: 31
  • Last Updated: 3 weeks ago

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