Defect detection in interferometers through supervised regression-based machine learning

This project focused on developing a machine learning solution for detecting and quantifying defects using specialized microscopic devices.

A supervised regression approach was employed, with the training dataset automatically generated by combining ideal images with synthetically added defects.

The system reliably identified defective areas and accurately calculated their sizes, with a particular focus on repeatable calculations.