## 5 Conclusions  
This study proposes a vision-based approach for detecting and predicting damage on optics using image segmentation. The deep learning system accurately identifies laser-induced damages on optics in real time and predicts their area based on future experiment parameters. To overcome the challenge of limited samples, a method combining ResNet and U-Net algorithms, along with data augmentation and image processing techniques, achieves precise damage detection. Normalizing sample brightness and contrast and using Braille markers for localization ensure consistency in studying area growth and accurate damage detection across samples. Unlike typical classification models that assign a single label to an entire image, the ResNet model employed in this approach assigns a class label to each pixel, enabling better localization of damages. Moreover, this detection model can be trained end to end with small samples without the need for manual labeling or feature extraction. This method also exhibits advantages over other detection methods when dealing with samples containing multiple adjacent objects. It effectively identifies false damages caused by reflections through spatial and intensity information, which improves efficiency and accuracy compared to previous studies using typical classification models. The proposed method demonstrates high effectiveness, with over 97% success rate in damage detection and less than 7% failure rate, mostly due to pixel loss. We can predicted the damage areas with less than 10% average relative error, making it suitable for online detection and maintenance in large laser facilities with limited samples. While promising for improving optic efficiency and longevity, the system's predictive ability relies on imaging system quality, and failure to detect Braille marks may affect accuracy. Moreover, it does  
Figure 19: **(a)** Prediction of the growth of a single damage; **(b)** prediction of the development of the total damages. not effectively utilize successive weekly images to discern tiny defects from backgrounds, suggesting potential enhancement through deep learning techniques for tracking and predicting damage growth. Optimizing the hardware conditions of the lighting system to ensure clear visibility of the four Braille markings and improving the current experimental process by utilizing idle time slots to capture online FODI images could further enhance the capabilities and reliability of the online optics damage detection system. This approach offers a new perspective for addressing the longstanding challenge of real-time laser optical damage prediction, advancing the field, and providing new solutions for the daily operation and maintenance practices of large-scale laser facilities. While our method requires further refinement, it holds the potential to enhance the reliability, efficiency, and lifespan of optics, thus promoting the widespread application of high-power laser technology in scientific and industrial domains. Future research can focus on improving the robustness of the algorithm and integrating continuous online images into the analysis pipeline to further enhance the performance and reliability of optics. Conceptualization, X.H. ([PERSON]) and Z.C. ; methodology, W.Z. ([PERSON]); software, H.G. ; validation, W.Z. ([PERSON]); formal analysis, Z.C. ; investigation, W.Z. ([PERSON]); resources, B.Z. ; data curation, B.Z. ; writing--original draft preparation, X.H. ([PERSON]); writing--review and editing, Z.C. ; visualization, W.Z. ([PERSON]); supervision, Q.Z. ; project administration, Q.Z. ; funding acquisition, X.H. ([PERSON]); formal analysis, X.H. ([PERSON]) and Z.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the NATURAL NATIONAL SCIENCE FOUNDATION OF CHINA (grant: 62105310). Data available on request due to restrictions (confidential experimental data). The data presented in this study are available on request from the corresponding author.