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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography.

Seungju Lee¹, Yoonjae Chung², Chunyoung Kim³

¹Department of Future Convergence Engineering, Kongju National University, 1223-24 Cheonan-Daero, Seobuk-gu, Cheonan-si 31080, Republic of Korea.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

Lock-in thermography (LIT) effectively detects thinning defects in S275 steel using advanced image processing. This non-destructive testing method offers reliable, real-time thermal analysis for material inspection.

Keywords:

array-type lamp automatic detection detectability evaluation image segmentation lock-in thermography morphology operation

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Area of Science:

Materials Science
Non-Destructive Testing (NDT)
Infrared Thermography

Background:

Infrared thermography (IRT) is a reliable non-destructive testing technique for real-time thermal response measurement.
Defect detection in materials is crucial for structural integrity and safety.

Purpose of the Study:

To detect thinning defects in S275 specimens using lock-in thermography (LIT).
To evaluate the automated detection and segmentation performance for defect characterization.

Main Methods:

Lock-in thermography (LIT) was applied to S275 specimens with thinning defects.
Four-point signal processing was used to acquire phase and amplitude images.
Otsu algorithm for binarization and boundary tracking for automated defect detection.
Root Mean Square Error (RMSE) was used to evaluate detectability.

Main Results:

Optimal excitation frequency was determined for LIT.
Automated detection and segmentation of thinning defects were achieved.
Defect detectability was quantitatively evaluated using RMSE.

Conclusions:

LIT is a viable technique for detecting thinning defects in S275 steel.
Image segmentation and automated detection methods enhance defect clarification.
RMSE provides a quantitative measure for evaluating defect detection performance.