## 2 Methods  
High-power laser irradiation on the FOA can cause complex physical and mechanical effects. When these effects accumulate to a certain extent, damage occurs, known as laser-induced damage (LID). The causes of damage are divided into four types: damage induced by inherent material defects, processing defects, surface contamination, and laser damage to optical thin films. In order to monitor damages on optics online, a final optic damage inspection (FODI) [28] system was designed for imaging the overall aperture of optics using side illumination, which is shown in Figure 1. The FODI system uses dark field split-time independent side illumination technology to achieve online optic imaging, capable of detecting damages larger than 100 \(\upmu\)m. However, the presence of stray light and other downstream optics can lead to the generation of false damage, which needs to be filtered out in the images. Figure 1 illustrates four main types of false damage: (1) damage reflection image, (2) hardware reflection, (3) damage from downstream optics, and (4) light spots. Hardware reflections, categorized as (2), are often observed at upstream optics such as continuous-contour phase plates (CPPs) and frequency conversion crystals (11 mm type-I KDP doubling crystals and 9 mm type-II deuterated KDP tripling crystals). Given the diverse formation mechanisms of false damage, it becomes challenging to physically distinguish authentic damage from false ones. To address this challenge, we adopted an offline approach instead of an online inspection to meet practical demands during weekly maintenance of the facility. Before installation or after removal, optics were placed into a dark box and subjected to side illumination to obtain offline dark-field images [29]. This procedure helped to eliminate the influence of background light or downstream optics. Consequently, any white dots observed in the offline image could be confidently identified as authentic damage, as shown in Figure 2.  
Given the constraints of maintenance time and labor costs, the availability of offline images for analysis is limited. Recently, fewer than 10 offline images have been directly related to their online counterparts, a quantity insufficient for traditional machine learning applications. Furthermore, the scheduling of optics maintenance in routine operations is restrictive, limiting the opportunities to acquire new offline images. To address this, optics owning more than 1000 damages were prioritized for offline high-definition imaging, maximizing the collection of valuable damage samples within the constraints. To overcome the challenge of limited data availability, we employed data augmentation [30] techniques. Data augmentation involves altering a limited dataset in various ways  
Figure 1: The schematic of the FODI system and FOA. Figure 2: Different types of false damage in online FODI images: (**a**) an offline image; all the damage can be regarded as real damage; (**b**) an online image. to create new, synthetic data points. This can include transformations such as rotation, scaling, and changes in lighting conditions, which help in simulating different scenarios that could lead to damage. By generating these new data points, we enhanced the robustness and generalization capabilities of our machine learning models. This approach allows for more effective training of models to identify and classify damage, improving their accuracy and reliability in real-world applications despite the initial scarcity of offline images. Braille marks [31] representing the last four digits of element numbers were engraved at the four corners of every optic, as depicted in Figure 3. These Braille marks served as reference points for orientation during the processing of FODI images. By aligning the FODI image with these marked corners, we ensured that each image was standardized to have the same detection coordinates. When referencing the offline image area, we located the corresponding damage points on the matching online image and then calculated their coordinates.