## 3 Implementation  
### Data Preprocessing  
Taking FODI images once a week for maintenance purposes introduces variability in environmental conditions, leading to differences in light intensity and contrast ratio  
Figure 3: Braille marks representing the last four digits of element numbers were carved at four corners of every optical element. between images. This variation can indeed affect the accuracy of damage area calculations. Figure 4 illustrates a single damage point captured over different time intervals, showcasing the variability in damage appearance. Furthermore, as depicted in the seventh picture of Figure 5 (taken on 7 September 2019), inconsistencies in light intensity and contrast ratio can result in discrepancies in pixel values for incremental damage. This deviation from the expected pattern contradicts the principle that incremental damage should not be irreversible. By incorporating these strategies, we can enhance the accuracy and reliability of damage area calculations in FODI images, enabling more effective maintenance and management of structural integrity. In order to solve the problem mentioned above, we have tried several algorithms, but no one worked well. Finally, we adopted the method of adjusting the light intensity coefficient of the entire image based on the Braille markers as a reference. The adopted solution involves using braille marks as references to adjust light intensity coefficients for entire images, with the following steps:  
Figure 4: The lack of conformity of brightness and contrast between photographs taken on different dates. Figure 5: The abnormal growth of the damage area caused by the inconsistency of image brightness and contrast. 1. Image Preprocessing: identical areas marked by braille marks were extracted from all images; 2. Grayscale Normalization: grayscale distributions within these areas were normalized to ensure consistency across images; 3. Criterion Selection: the image with the maximum grayscale value (\(pi_{\text{upper}}\)) was selected as the criterion; 4. Grayscale Adjustment: grayscale intervals of other images were adjusted to match the criterion image; 5. Coefficient Calculation: each image was assigned its adjustment coefficient (\(co(i)\)), likely based on its grayscale values relative to the criterion image; 6. Adjustment Application: the pixel values of each image were multiplied by their respective adjustment coefficients to achieve uniform brightness values across the image set. Figure 6 and Equation (1) illustrate the process of cutting identical areas marked by Braille marks from all images, obtaining the normalized distribution of pixel grayscale in these areas. Subsequently, the image with the maximum grayscale value was selected as a criterion, denoted as \(pi_{\text{upper}}\). The grayscale intervals of other images were adjusted to match this criterion, and their adjustment coefficients, denoted as \(co(i)\), were calculated accordingly. Ultimately, the pixel values of the remaining complete images were multiplied by their corresponding adjustment coefficients to achieve a uniform set of brightness values across all images. Equation (1) defines the variables used in the adjustment process, where n represents the number of pictures, \(pi_{\text{adj}}\) denotes the pixel value after adjustment, \(pi_{\text{ori}}\) represents the pixel value before adjustment, and \(pi_{\text{ori}}\) signifies the total number of pixels in the \(i\)-th picture. \[pi_{\text{upper}}(0)=\max\{pi_{\text{upper}}(1),pi_{\text{upper}}(2),pi_{\text {upper}}(3),\ldots,pi_{\text{upper}}(n)\}\]  
\[co(i)=pi_{\text{upper}}(i)/pi_{\text{upper}}(0)\hskip 28.452756 pt(i\in\{1,n\})\]  
\[pi_{\text{adj}}(i)(i)=pi_{\text{ori}}(i)(i)/co(i)\hskip 28.452756 pt(j\in\{1,m_{ \text{i}}\})\]  
Next, we proceeded to adjust the contrast of the image. While achieving complete uniformity in contrast values may not be feasible, our objective was to equalize the low-contrast areas to bring them closer in value.