Abstract Purpose We present a comparative study of segmentation methods for high-power laser applications, focusing on two specific challenges: detection of microscopic surface damage on optical components and detection of radiochromic films for reconstructing high-dimensional particle phase space distributions. Methods Both applications involve complex morphological variations and non-homogeneous contrast conditions, requiring robust and scalable analysis methods. We evaluate two conventional algorithms and two deep learning-based instance segmentation models, including YOLOv8n-seg and a Detectron2-based Mask R-CNN implementation. All models are evaluated on real datasets that reflect the experimental complexities. We focus particular attention to the accuracy of contour detection, using geometric evaluation metrics such as radial contour comparison, Hausdorff distance, Chamfer distance, as well as intersection-over-union, and analysing runtime performance. Results Our results indicate that the YOLOv8n-seg model outperforms the conventional surface damage segmentation method in accuracy, but with 12 times higher computational requirements. In contrast, for radiochromic films analysing YOLOv8n-seg achieves both higher accuracy and faster evaluation. In comparison to YOLOv8n-seg model, Detectron2-based Mask R-CNN implementation lags in both segmentation performance and runtime. Conclusion These results highlight the potential of YOLOv8n-seg model in addressing specific data-related challenges in modern laser diagnostics and support their role in the development of next-generation automated analysis systems.
Pietzsch et al. (2026) studied this question.