## 1 Introduction  
The final end of the inertial confinement fusion (ICF) [1; 2; 3; 4; 5] system, known as the final optic assembly (FOA) [6; 7; 8; 9], is subjected to the highest laser power load within the entire optical system. The damage-bearing capacity of optics in the FOA is severely limited by the nonlinear effects of high-power laser radiation, material defects, and other forms of contamination. The load capacity of optics, often referred to as their damage-bearing capacity, is closely linked to the output laser power and energy stability of high-power laser facilities. For these new and qualified optics, initial damage to optics spans from dozens of micrometers to several millimeters [10; 11; 12; 13; 14]. The earlier we detect and analyze such minor damage, the more likely we are to restore or replace the affected elements promptly, thereby effectively extending the longevity of the optics. Conversely, if the accumulating damage spirals out of control, it could result in significant economic losses and even induce laser beam modulation, leading to a catastrophic disaster for the entire facility [15; 16; 17; 18]. The traditional method of detection involves the use of microscopes; this method consumes a significant amount of time and manual labor, and its reliability is often unsatisfactory. Additionally, it is limited by environmental conditions, presenting significant drawbacks that are incompatible with the experimental requirements of high-power laser facilities. Consequently, we have turned to using imaging devices such as CCD [19], optical coherence tomography (OCT) [20], photoluminescence (PL) [21], etc., to capture optic images. Subsequently, image processing technology has been integrated with computer vision, specifically machine vision, to develop a more effective detection method. The image processing algorithm based on the morphology of graphics has found wide applications across various fields. However, it faces challenges when dealing with environments filled with unpredictable noise, leading to inefficiencies and inaccuracies in damage detection. Currently, a novel image processing algorithm based on deep learning is emerging as a promising alternative. Deep learning enables a closer approximation between complex input and output functions due to its self-learning capabilities and hierarchical structure. In comparison to morphology-based methods, deep learning has the potential to significantly enhance the algorithm for target detection [22]. In 2008, [PERSON] from Lawrence Livermore National Laboratory (LLNL) was among the first to introduce machine learning technology into damage classification, focusing on the identification and characterization of damage occurrences [23]. Two years later, [PERSON], also from LLNL, applied a similar algorithm to recognize false damage caused by hardware reflections [24]. In 2014, [PERSON] and her team from Zhejiang University utilized a classic machine learning algorithm, a support vector machine, for offline detection in the surface defects evaluating system (SDES), successfully distinguishing microscale damage and dust [25]. Subsequently, in 2019, [PERSON] from Harbin Institute of Technology conducted research on an intelligent inspection method for detecting weak feature damage in large aperture final optics using the kernel-based extreme learning machine (K-ELM) algorithm [26; 27]. However, it is worth noting that the damage samples used in both the training and test sets were sourced from a single picture, indicating a need to enhance the generalizability of the research. This study outlines a predictive approach to incremental damage on optics. Initially, we introduce our proposed solution to the challenging engineering issues related to damage detection. Subsequently, we employ suitable algorithms to develop mathematical models for this purpose. Finally, we conclude by detailing the research progress and achievements in the field of predictive technology for damage prediction on optics.