Research data on DKDP crystal laser damage detection based on machine learning and image processing

Variations in image detection techniques during DKDP crystal laser-induced damage testing often introduce errors in image processing. To address this challenge, a novel detection method integrating machine learning and image processing is proposed, enabling rapid identification of diverse damage patterns and precise quantification of critical parameters (e.g., damage point counts and densities) in DKDP crystal images.

By leveraging local two-dimensional pattern features extracted from crystal damage images, a Support Vector Machine (SVM) is employed for damage-type classification. For heterogeneous damage images, advanced image processing techniques—including image differencing, high-pass filtering, and high-boost filtering—are applied to significantly enhance the local contrast of subtle damage features. Thresholding and regional image segmentation methods are further utilized to extract edge pixels of damage points, enabling systematic classification, labeling, and statistical analysis.