Unsupervised Crack Detection in X-ray Tomography using SAM

02 — Approach

Method

The workflow below is a visual summary of the documented implementation; it is not a claim of additional system components.

1. 01

   Designed an unsupervised crack detection pipeline using FFT-based frequency-domain filtering to automatically identify crack regions and generate bounding box prompts for SAM, enabling annotation-free defect localization

   →
2. 02

   Refined crack boundary segmentation using SAM conditioned on FFT-derived prompts, improving spatial precision of detected defects in high-noise X-ray tomography images

   →
3. 03

   Characterized the sensitivity of segmentation quality to FFT frequency thresholds and SAM prompt density, establishing reliable operating ranges for robust defect detection

03 — Contribution

Contribution

• 01

  Designed an unsupervised crack detection pipeline using FFT-based frequency-domain filtering to automatically identify crack regions and generate bounding box prompts for SAM, enabling annotation-free defect localization

• 02

  Refined crack boundary segmentation using SAM conditioned on FFT-derived prompts, improving spatial precision of detected defects in high-noise X-ray tomography images

• 03

  Characterized the sensitivity of segmentation quality to FFT frequency thresholds and SAM prompt density, establishing reliable operating ranges for robust defect detection

04 — Evidence

Results

This project record verifies the delivered implementation, but does not report a comparable performance metric.