## 4 Experimental Results and Discussion
In addition, there was still a problem of misclassification and over-fitting, and the MobileNet model without data augmentation was found to be the model with better performance, having lower recall due to the lower quantity of images labeled as \"crack\" images compared to the number of samples labeled \"non-crack\" images, as shown in Table 5.  
Table 6 depicts the test performance of the proposed algorithms on the test dataset samples. It can be seen that both the InceptionV3 + DA and MobileNet + DA models were able to predict the categories successfully with an error rate of zero (0). Table 5 shows detection and classification performance measures for the crack and non-crack type classes in the sample images.The proposed frameworks (Alexnet, InceptionV3, LAECNet, and MobileNet) analyzed cracks with F1 scores of \(97\%,100\%,93\%,\) and \(100\%\), respectively, with the DA techniques, and for non-crack samples they achieved F1 scores of \(99\%,100\%,97\%,\) and \(100\%\), respectively. However, without DA training techniques and with fewer crack image samples, they obtained scores of \(80\%,86\%,76\%,\) and \(93\%\). To minimize the problems of misclassification and over-fitting, and to improve their recall, we implemented DA techniques to improve the performance and over-fitting of the classifiers with 100 epochs and a BS of 16 images. The LAECNet architecture improved its accuracy, F1, precision, and recall by \(4\%,10\%,6\%,\) and \(11\%\), respectively. The AlexNet architecture also improved its accuracy, F1, precision, and recall by \(5\%,5\%,5\%,\) and \(5\%\), respectively; The InceptionV3 architecture improves its accuracy, F1, Precision, and Recall by \(7\%,7\%,6\%,\) and \(7\%\), respectively. The MobileNet architecture improved its accuracy by \(5\%\). As shown in Table 5, the MobileNet classifier achieved improved F1, precision, and recall scores of \(100\%,100\%,\) and \(100\%\), respectively, which were superior to those of the other seven (7) classifier models trained with 100 epochs and a BS of 16 images on the augmented dataset. Table 3 shows the values of the diverse hyper-parameters of the proposed MobileNet architecture with and without DA. These parameters were chosen based on a comparison between the other proposed models such as LAECNets and the other pre-trained net architectures in order to fix the ability of each neural network architecture to identify/detect and classify cracks in automotive engine components using samples of test images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **TP** & **FP** & **FN** & **TN** & **Error Rate** \\ \hline AlexNet & 7 & 1 & 1 & 19 & 0.0714 \\ AlexNet + DA & 51 & 0 & 3 & 111 & 0.018 \\ InceptionV3 & 6 & 2 & 0 & 20 & 0.071 \\ InceptionV3 + DA & 51 & 0 & 0 & 20 & 0.071 \\ LAECNet & 8 & 0 & 5 & 15 & 0.179 \\ LAECNet + DA & 50 & 1 & 6 & 108 & 0.042 \\ MobileNet & 7 & 1 & 0 & 20 & 0.035 \\ MobileNet + DA & 51 & 0 & 0 & 114 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test performance of the proposed crack classifiers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Classes** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{AlexNet} & Crack & 88 & 881 & 88 \\  & Non-Crack & 95 & 95 & 95 \\  & Crack & 94 & 100 & 97 \\ AlexNet + DA & Non-Crack & 100 & 97 & 99 \\  & Crack & 100 & 75 & 86 \\ InceptionV3 & Non-Crack & 91 & 100 & 95 \\  & Crack & 100 & 100 & 100 \\ InceptionV3 + DA & Non-Crack & 100 & 100 & 100 \\  & Crack & 62 & 100 & 76 \\ LAECNet & Non-Crack & 100 & 75 & 86 \\  & Crack & 89 & 981 & 93 \\ LAECNet + DA & Non-Crack & 99 & 95 & 97 \\  & Crack & 100 & 88 & 93 \\ MobileNet & Non-Crack & 95 & 100 & 98 \\  & Crack & 100 & 100 & 100 \\ MobileNet + DA & Non-Crack & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results for each class type.