## 4 Experiments and Results
### Corrosion Level Classification Network Experiment and Analysis  
The core of the cascaded architecture proposed in this paper is the corrosion level determination performed by the YOLOv10-vit-cls network within the corrosion window detected by the YOLOv10-vit network. The YOLOv10-vit-cls network constructed in this paper was validated on the corrosion level determination dataset and compared with several networks proposed during the algorithm optimization process. The networks used in the corrosion level determination comparison experiments are described as follows: YOLOv8n-cls: a classification network based on YOLOv8 and implemented by the Ultralytics team; YOLOv10n-cls: a network built following the same steps as YOLOv10-vit-cls, with the only difference being that it is built on the YOLOv10n backbone; YOLOv10n-SP-cls: a version of YOLOv10n-cls that retains the Spatial Pyramid Pooling Fast (SPPF) layer from the 11 th layer and the PAS from the 12 th layer of the backbone; YOLOv10n-SP-vit-cls: a version of YOLOv10-vit-cls that retains the SPPF and PAS layers from the YOLOv10n backbone; YOLOv10n-SP-vit-cls-TL and YOLOv10n-vit-cls-TL: are versions of YOLOv10n-SP-vit-cls and YOLOv10n-vit-cls that undergo transfer learning based on the YOLOv10-vit backbone. The accuracy performance of each network on the validation set is shown in Table 5. The model with the lowest accuracy was YOLOv10n-SP-cls, which performed worse than YOLOv10n-cls, lacking the SPPF and PAS structures. Using the MobileViTv3 block, the YOLOv10n-vit-cls model achieved a 4.9% improvement over YOLOv10n-cls. After transfer learning, the accuracy of YOLOv10n-vit-cls-TL was further enhanced by 3.9%, reaching an accuracy of 0.917. The loss curves of different networks on the training set are shown in Figure 8a, with three main concentrated curve regions. The first part consists of networks that do not integrate the MobileViTv3 block and those that retain the SPPF and PAS structures. The convergence speeds of these networks are similar. The second part includes only the YOLOv10-vit-cls network's loss curve. It can be observed that the loss curve of the YOLOv10-vit-cls network, which integrates the MobileViTv3 block, shows a significantly faster downward trend compared to YOLOv10n-cls, indicating that the MobileViTv3 block positively impacts the network's convergence speed. The third part consists of YOLOv10 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **YOLOv8n-cls** & **YOLOv10n-SP-cls** & **YOLOv10n-cls** & **YOLOv10n-SP-vis-cls** & **YOLOv10n-SP-vis-cls-TL** & **YOLOv10n-** \\ \hline Acc & 0.84 & 0.818 & 0.829 & 0.829 & 0.878 & 0.901 & 0.917 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of accuracy metrics for different models on the validation set. The accuracy curves of the different networks on the validation set are shown in Figure 8b. The distribution trend is quite similar to that of the loss curves on the training set, with YOLOv10-vit-cls-TL achieving the highest accuracy. The confusion matrix is a common method for evaluating the performance of classification models. Figure 9 shows the normalized confusion matrix for each network based on YOLOv10 on the validation set. Since the sample sizes of different classes may vary, the normalized confusion matrix divides each element of the matrix by the corresponding true sample count of that class, resulting in the classification accuracy for each class. The higher the value, the darker the color. The values on the main diagonal of the confusion matrix represent true positives (TP), so the values on the diagonal of the normalized confusion matrix represent the recall for each class. Figure 8: Loss curves on the training set and accuracy curves on the validation set during the training process. Figure 9: Confusion matrices of various models based on YOLOv10. For the confusion matrix of YOLOv10-vit-cls-TL in Figure 9f, images with corrosion level A in the validation set are all correctly predicted, with no false negatives.