## 4 Result Exploration and Argument
Figure 14 shows the PR curve of different TL models in our works. In this figure, we mark four curves of red, green, blue, and orange colored for AlexNet, DenseNet, VGG16, and ResNet18 models. In the upper side of this figure, all curves are looking together. For understanding better, we observed it as zoom out which \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Train/Test** & **Max Acc (\%)** & **MA\_E** & **Min Acc (\%)** & **MinA\_E** & **Avg\_acc (\%)** \\ \hline TL VGG16 & Train & 99.76 & 30 & 98.06 & 1 & 99.61 \\  & Test & 99.86 & 29 & 99.65 & 17 & 99.78 \\  & Train & 99.09 & 29 & 95.31 & 1 & 98.74 \\ TL ResNet18 & Test & 99.41 & 18 & 98.09 & 1 & 99.22 \\  & Train & 99.51 & 25 & 96.68 & 1 & 99.24 \\ TL DenseNet161 & Test & 99.68 & 27 & 99.29 & 1 & 99.60 \\  & Train & 99.85 & 24 & 98.34 & 1 & 99.72 \\  & Test & 99.90 & 13 & 99.58 & 20 & 99.84 \\  & Train Max Acc & 99.85 & & & TL AlexNet at Epoch 24 & \\ All & Test Max Acc & 99.90 & & & TL AlexNet at Epoch 13 & \\  & Both Max Acc & 99.90 & & & TL AlexNet at Epoch 13 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance measurement of used TL methods during 30 epochs of training where MA_E = Maximum Accuracy at epoch, MinA_E = Minimum Accuracy at epoch, Avg_acc = Average Accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & & **P (\%)** & **R (\%)** & **F1 (\%)** & **Sup** & **A (\%)** & **MCC (\%)** & **CK (\%)** \\ \hline TL VGG16 & Crack & 99.73 & 99.90 & 99.81 & 4000 & & 99.81 & 99.6252 & 99.6250 \\  & Non-crack & 99.90 & 99.73 & 99.81 & 4001 & & & \\ TL ResNet18 & Crack & 98.96 & 99.70 & 99.33 & 4000 & & 99.33 & 98.6529 & 98.6502 \\  & Non-crack & 99.70 & 98.95 & 99.32 & 4001 & & & \\ TLDenseNet161 & Crack & 99.50 & 99.85 & 99.68 & 4000 & & 99.68 & 99.3507 & 99.3501 \\  & Non-crack & 99.85 & 99.50 & 99.67 & 4001 & & & \\ TL AlexNet & Crack & 99.92 & 99.80 & 99.86 & 4000 & & 99.86 & 99.7251 & 99.7250 \\  & Non-crack & 99.80 & 99.93 & 99.86 & 4001 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Various scores were calculated in the test dataset (CCIC) for different TL models after 30 epochs of training where P = Precision, R = Recall, FI = F1-score, Sup = Support, A = Accuracy, CK = Cohen’s Kappa. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Duration Per-Epoch** & **Remarks** \\  & **(h:mm:ss)** & **(h:mm:ss)** \\ \hline TL VGG16 & 0:08:46.729322 & 3 rd place \\ TL ResNet18 & 0:03:35.636223 & 2 nd place \\ TL DenseNet161 & 0:13:39.103467 & Lowest place \\ TL AlexNet & 0:02:53.093954 & 1 st place \\ TL AlexNet takes the 1 st position by achieving the least training time \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training time per-epoch of TL models. is shown in the down portion of the figure. From this figure, we can see that all models' performance is good. In addition, we used the external dataset Building Wall Crack Images (BWCI) from Kaggle to validate our models. This is an open-source dataset. BWCI consists of wall crack images with 27 \(\times\) 27 pixels. Table 6 shows the description of the dataset and a few samples are shown in Figure 15.  
Figure 14: PR curve of different TL models. Figure 13: ROC curve of different TL models. Two statistical tests have been carried out, named the Matthews correlation coefficient (MCC) and Cohen's Kappa Statistic [49] for comparing the performance. Matthews correlation coefficient (MCC) is a popular performance metric that is used in the case of an imbalanced dataset. Although the utilized dataset in this paper is a balanced dataset, it is defined by the following mathematical equation number 5. \[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)\times(TP+FN)\times(TN+FP) \times(TN+FN)}} \tag{5}\]  
The range of MCC is [\(-\)1-1]. The value of MCC is near to 1 is better. All of the utilized models perform well. Their values are near to 1. That means, the models classified the crack images accurately.