# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
## 3 Damage Detection Methods
### Analysis Results  
The damage detection analysis was performed on 27,139 images, comprising 20% of the total 135,696 images collected in this study. Figure 10 compares detection rates of algorithms we tested. BIRD U-Net exhibited a higher detection recall than U-Net and ResNet in all test parts for the analysis of each damage item, with 96.31% for cracks, 94.98%  
Figure 8: Verification process.  
Figure 9: Confusion matrix.  
for delamination, 97.32% for rebar exposure, 94.98% for leakage efflorescence, and 95.39% for corrosion.  
Regarding U-Net, detection recalls of 94.83% for cracks, 91.86% for delamination, 95.51% for rebar exposure, 88.30% for leakage efflorescence, and 90.04% for corrosion were verified, and the recall was lower than that of BIRD U-Net in all five categories.  
Conversely, when the ResNet algorithm was used to verify the pixel-by-pixel classification for the five categories (cracks, delamination, rebar exposure, leakage efflorescence, and corrosion), it had a relatively lower detection rate than BIRD U-Net, with 75.47% for cracks, 74.52% for delamination, 75.75% for rebar exposure, 67.92% for leakage efflorescence, and 63.22% for corrosion.  
The recall, error, and detection rates of the deep learning analysis methods reviewed in this study are demonstrated in Table 3. Noticeably, the segmentation method demonstrated better performance in detecting damage than the detection method, and the overall recall rate was lower in the case of relatively small leaks in the collected damage images.  
The performance was next evaluated using a chaotic matrix-based model evaluation method to determine the suitability of the technology for deep learning-based automatic damage analysis. In addition, the BIRD U-Net, U-Net, and ResNet algorithms were trained under the same conditions with the training data to perform the analysis and compare the performance of the three models. Accordingly, BIRD U-Net exhibited an average recall of more than 95%, whereas U-Net exhibited an average recall of more than 92%. On the contrary, the ResNet model algorithm exhibited an average recall of 71%, and the BIRD U-Net model algorithm outperformed it by more than 24%. Notably, BIRD U-Net and U-Net showed the most substantial improvements in detection recall by damage type, with  
Figure 10: Detection rate comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Cracks** & **Scaling/Spalling** &
\begin{tabular}{c} **Reinforcement** \\ **Exposure** \\ \end{tabular} & **Leaks** & **Corrosion** \\ \hline BIRD U-Net & 0.9631 & 0.9498 & 0.9732 & 0.9498 & 0.9539 \\ \hline Segmentation method (U-Net) & 0.9483 & 0.9186 & 0.9551 & 0.883 & 0.9004 \\ \hline ResNet & 0.7547 & 0.7452 & 0.7575 & 0.6792 & 0.6322 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis results for each type of damage.  
efflorescence at 7% and corrosion at 5%. In comparison, cracks, delamination, and rebar exposure averaged a 2% increase.  
It is important to note that, like many other deep learning models, the BIRD U-Net algorithm does not have a fixed or specific number of images it requires to operate effectively. The number of images necessary for training depends on multiple factors, including the complexity of the problem, the diversity of the dataset, and the desired model performance. There is no rigid rule for the number of images, and data requirements can significantly vary from one project to another. We are convinced that the U-Net algorithm is a superior choice for the purpose of bridge damage detection. Therefore, we are committed to further research aimed at enhancing the performance of the U-Net algorithm.