## 2 Materials and Methods
### CNN Architecture
#### 2.1.1 ResNet Network Architectures  
In ResNet architecture, some inputs are passed directly to the next layers as opposed to the sequential layers in regular CNNs (Figure 1), allowing for deeper networks to be trained without the problem of vanishing points [32], resulting in an increase in model accuracy. As indicated in Figure 1a, the output F(x) is not transported to the next block  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Model** & **Pre-Trained on** & **Dataset** & **Defect Type** & **Accuracy** \\ \hline [PERSON] et al. & & & & \\
[14] & AlexNet & IMAGENET & SDNET & Binary Crack & 95.52\% \\ \hline [PERSON] & & & & \\  & & & & \\  & & & & \\ \hline [PERSON] and [PERSON] & & & & \\
[23] & EfficientNetB0 & IMAGENET & [PERSON] and [PERSON] \& SDNET & Binary Crack \\ \hline [PERSON] et al. & & & & \\
[16] & DINO ViT/8 & - & SOFIA & Multiple defects & 89.20\% \\ \hline [PERSON] et al. & & & & \\
[24] & Meta-learned & - & CODEBRIM & Multiple defects & 72.19\% \\ \hline [PERSON] \& [PERSON] [25] & AlexNet & IMAGENET & Concrete Images & Binary Crack & 89.00\% \\ \hline [PERSON] et al. [26] & \
\begin{tabular}{c} Customized \\ CNN \\ \end{tabular} & IMAGENET & SDNET, METU & Binary Crack & 98.30\% \\ \hline [PERSON] et al. [27] & VGG16 & IMAGENET &
\begin{tabular}{c} METU, SDNET, \\ BCD \\ \end{tabular} & Binary Crack & 99.80\% \\ \hline [PERSON] et al. [28] & Inception-v3 & IMAGENET & Self-collected & Multiple defects & 97.80\% \\ \hline [PERSON] et al. & & & & \\
[29] & VGG16 & IMAGENET & Self-collected & Multiple defects & 97.10\% \\ \hline [PERSON] et al. & \begin{tabular}{c} VGG16, \\ Inception-v3, \\ ResNet0 \\ \end{tabular} & IMAGENET & \
\begin{tabular}{c} CDS, \\ SDNETv1, \\ BCD, ICCD, \\ MCDS, \\ CODEBRIM \\ \end{tabular} &
\begin{tabular}{c} Multiple \\ defects \\ \end{tabular} & 90.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of CNN-based approaches for concrete defect detection, and classification accuracy values represent the highest reported result considering the best-performing model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Name** & **Total Images** & **Defect/Classes** \\ \hline ImageNet [13] & +15 M & Over 22,000 object classes \\ \hline SDNET [14] & \begin{tabular}{c} 230 High-resolution (56,092 \\ images for all classes) \\ \end{tabular} & Concrete cracks from bridges, walls, pavement \\ \hline CODEBRIM [15] & \begin{tabular}{c} 1590 High-resolution (10,789 \\ images for all classes) \\ \end{tabular} & \begin{tabular}{c} Multi-label/Cracks, corrosion, \\ spalling, etc. \\ \end{tabular} \\ \hline SOFIA [16] & 139,455 (53,805 are labeled) & \
\begin{tabular}{c} Bridge components and structural \\ defects \\ \end{tabular} \\ \hline METU [17] &
\begin{tabular}{c} 458 High-resolution (40,000 \\ images for all classes) \\ \end{tabular} & Crack/No-crack \\ \hline BCD [18] & 6069 & Bridge cracks/background \\ \hline CDS [19] & 1028 & Surface damages in concrete bridges \\ \hline ICCD [20] & 1455 & Cracks in bridges and towers \\ \hline MCDS [21] & 3607 & Various concrete defects \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some of the deep learning datasets and bridge databases.  
until it is combined with x and put into ReLu activation function in the residual block [32]. Figure 1b shows one kind of residual block used in ResNet-18, and Figure 1c shows the other residual block used in ResNet-50/101. In this study, we choose the ResNet-18/50/101 model for the train and test sets, which architecture is shown in Figures 2 and 3.