## 4 Experimental Results and Discussion  
Eight architectural classifiers were proposed and trained using four approaches (LAECNet, AlexNet, InceptionV3, and MobileNet) and two datasets (with and without DA) were used. Then, classifiers were utilized to sort and classify the samples of images in the test dataset. Figures 8 and 9 reveal the loss and training accuracy during validation and training per epoch of the suggested algorithms. All the architectures were trained with 100 epochs and a BS of 16 images on both datasets. The detection process was carried out using a modified YOLOv5s [62] object detector with epochs of 128 and 196, a batch size of 2, and a threshold of 0.5. The performance evaluation indicators used were accuracy, F1, precision, error rate, mAP, and recall, and these were calculated using Equations (4)-(11). \[I(x,y)= \begin{cases} Crack,&\text{if }Value<0\\ Non-Crack,&\text{otherwise}\end{cases}  \tag{12}\]  
Using Equation (12), the models' ability to predict whether the input image I(\(x\),\(y\)) was cracked or non-cracked can be demonstrated. We noted that the models predicted the best possible outcomes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Without DA** & **With DA** \\ \hline Weight Decay & \(5\times 10^{-4}\) & \(5\times 10^{-4}\) \\ Momentum & 0.90 & 0.90 \\ Iterations per Epoch & 7 & 38 \\ Maximum Iterations & 700 & 3800 \\ Batch Size & 16 & 16 \\ Maximum Epochs & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different hyper-parameters of the proposed algorithm. Figure 8 summarizes the loss (upper) and accuracy (lower) changes during the training of the proposed algorithms. As shown in Figure 8 (upper portion), these validation and training losses fluctuated, with the losses fluctuating separately across the epochs. The LAECNet algorithm showed a noticeable gap between its two-loss curves, indicating an over-fitting problem (unrepresentative training data compared to validation data). In the same graphs, the Alexnet algorithm also exhibited some over-fitting due to unrepresentative validation data compared to the training data samples. However, Figure 8 (lower portion) indicates training convergence, with a decline in losses and an increase in accuracy. Figure 9 summarizes both the loss and accuracy of the algorithms obtained when using the DA techniques. Figure 8: Losses (**upper portion**) and accuracy (**lower portion**) during training and validation. Figure 9: Losses (**upper**) and accuracy (**lower**) during training and validation using DA techniques. As shown in Figure 9 (upper portion), the algorithms still exhibited the problem of some over-fitting in the case of LAECNet + DA and AlexNet + DA. In addition, the InceptionV3 + DA and MobileNet + DA algorithms also showed some under-fitting. However, the algorithms with DA achieved better classification accuracy compared to the algorithms without DA. Among the ConvNets trained with 100 epochsand with a batch size of 16 (Table 2), on the dataset without DA, the LAECNet classifier achieved accuracy, F1, precision, and recall values of \(89\%,83\%,89\%\), and \(82\%\), respectively; the AlexNet classifier achieved accuracy, F1, precision, and recall values of \(93\%,93\%,93\%,93\%\), and \(0.93\), respectively; and the InceptionV3 classifier achieved accuracy, F1, precision, and recall values of \(93\%,93\%,94\%\), and \(93\%\), respectively. However, the MobileNet ConvNet achieved better classification accuracy, F1, precision, and recall values of \(96\%,96\%,97\%\), and \(96\%\), respectively. Table 4 and the accuracy graph presented in Figure 10 indicate that were was robustness among the results of the rest of the classifiers.