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Summary

This summary is machine-generated.

This study introduces a deep learning method to reliably detect defects in optical fringe patterns for non-destructive testing. The approach uses phase gradients to accurately locate defects in interferometric metrology.

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Area of Science:

Optical Metrology
Non-Destructive Testing
Artificial Intelligence

Background:

Optical fringe patterns are vital for non-destructive testing.
Detecting defects in these patterns is a significant challenge.
Current methods may lack robustness and precision.

Purpose of the Study:

To develop a deep learning-based method for accurate fringe pattern defect identification.
To improve defect localization using phase gradient information.
To demonstrate the method's effectiveness in various conditions.

Main Methods:

A deep learning model was employed to compute spatial phase derivatives.
Defect information was attributed to the fringe pattern's phase gradient.
The resulting gradient map was used for defect localization.

Main Results:

The method demonstrated robustness on numerically synthesized defects across different noise levels.
Successful experimental defect identification was achieved using diffraction phase microscopy.
The deep learning approach effectively localized defects in fringe patterns.

Conclusions:

The proposed deep learning method offers a reliable solution for fringe pattern defect detection.
Phase gradient analysis via deep learning enhances defect localization accuracy.
The technique shows practical utility in experimental metrology applications.