# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 3 Results
### Experimental Data
#### 3.3.1 Accuracy
(**a**) Comparison of accuracy of each model before data augmentation. (**b**) Comparison of the highest accuracy of each model before data augmentation. Figure 11: Comparison of the accuracy of each model before and after data augmentation. From the comparative analysis in Figure 11, it can be seen that after data augmentation, the accuracy of each model is lower than that before the data augmentation. Among the models, the accuracy of AlexNet and InceptionA decreases slightly after data augmentation. Although the accuracy of ResNet18 fluctuates greatly, the highest accuracy is not much lower than before the data augmentation. This shows that these three types of network models have strong generalization ability. LeNet5, InceptionE, and MobileNet have a large decrease in accuracy after data augmentation, which is lower than other models in generalization ability. The accuracy of Vision Transformer drops the most after data augmentation, indicating that its generalization ability in the field of computer vision is lower than other models. However, its accuracy rate can still reach a relatively high level of approximately 87%, indicating that it still has good development prospects in the field of computer vision. The accuracy comparison of each model for 10 epochs is illustrated in Figure 12a and Table 5.  
From the analysis of Figure 12a and Table 5, it can be seen that AlexNet, InceptionA, and ResNet18 still maintain a high level of accuracy after data augmentation, all of which can reach more than 99%. Among them, AlexNet has the highest accuracy, which can reach 99.7%. After several rounds of training, LeNet and InceptionE can reach an accuracy rate of more than 98%. The accuracy of MobileNet and Vision Transformer is significantly lower than that of the other models. In addition, the accuracy of ResNet18, MobileNet, and Vision Transformer fluctuates greatly, and the model stability is low. The accuracy of InceptionE  
Figure 12: Comparison of accuracy after data augmentation. (**a**) Comparison of accuracy of each model after data augmentation. (**b**) Comparison of the highest accuracy of each model after data augmentation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epoch** & **LeNet5 (\%)** & **AlexNet (\%)** & **InceptionA (\%)** & **InceptionE (\%)** & **ResNet18 (\%)** & **MobileNet (\%)** & **Vision Transformer (\%)** \\ \hline
1 & 96.875 & 98.838 & 93.912 & 71.050 & 86.625 & 83.412 & 80.912 \\
2 & 96.475 & 98.350 & 98.662 & 80.787 & 90.763 & 87.725 & 78.375 \\
3 & 97.050 & 99.287 & 98.463 & 91.425 & 95.375 & 83.825 & 81.075 \\
4 & 97.675 & 99.537 & 98.700 & 96.850 & 96.812 & 88.088 & 80.537 \\
5 & 96.412 & 99.638 & 98.850 & 98.138 & 92.338 & 89.450 & 86.550 \\
6 & 97.675 & 99.463 & 99.050 & 98.112 & 95.800 & 86.075 & 80.388 \\
7 & 97.062 & 99.700 & 99.175 & 98.388 & 99.025 & 95.713 & 85.062 \\
8 & 98.088 & 99.562 & 99.175 & 98.412 & 95.600 & 90.125 & 87.812 \\
9 & 97.950 & 99.612 & 99.125 & 98.438 & 92.700 & 96.612 & 82.888 \\
10 & 98.100 & 99.675 & 99.200 & 98.562 & 96.612 & 95.112 & 77.550 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of each model after data augmentation. continues to increase, so it is speculated that it has not reached its highest value of accuracy. After increasing the number of training rounds, the highest accuracy rate of the InceptionE model is 98.800%. Therefore, the highest accuracy of the InceptionE model is 98.800% in comparison to the highest accuracy of other data models. The comparison is illustrated in Figure 12b. From the analysis of Figure 12b and Table 5, it can be seen that the accuracy rate of AlexNet after data augmentation is the highest among the seven models at 99.700%. Compared with the accuracy before data augmentation, the decrease in accuracy is the smallest, which means that when the dataset is relatively small, AlexNet has a strong generalization ability and high model stability.