## 4 Data Validation
### Analysis of Results  
In order to verify the effectiveness and necessity of data migration between different bridges, the recognition effect of the model trained by using the target bridge training set \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Category** & **Number of Images** \\ \hline normal & 1463 \\ drift & 4621 \\ local gain & 244 \\ noise & 21,293 \\ missing & 10,503 \\ outlier & 1524 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The specific data volumes for each category of the B bridge dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Category** & **Number of Images** \\ \hline normal & 21,510 \\ drift & 162 \\ local gain & 21 \\ noise & 45 \\ missing & 202 \\ outlier & 398 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The specific data volumes for each category of the C bridge dataset. with extended data sets is compared with the recognition effect of the model trained by all the target bridge data training set, so that the Accuracy of the proposed problem can be verified. To illustrate the quality of the expanded dataset of A bridge, firstly, the expanded dataset is trained using the Resnet 50 model alone, and the dataset will be divided into a training set and a validation set according to the ratio of 8:2. Figure 4 shows the accuracy and loss function curves obtained during the training process, where the horizontal coordinate is the number of iterations of model training and the vertical coordinate is the Accuracy. From Figure 4, we can see that the Accuracy is high when the initial iteration of the convolutional neural network is performed, which means that the training model of this dataset has good classification ability for six categories of data. The A bridge dataset can be used as an expanded training set. The data volume of each category in the B bridge training dataset is shown in Table 2, from which it can be seen that the local gain data volume is seriously low, and the data is unbalanced. The Resent 50 model is used to train the data, and the hyperparameters are manually adjusted and set empirically. MaxEpoch is set to 1, InitialLearnRate is set to 0.001, Optimizer is set to SGD, and MiniBatchSize is set to 32. Figure 5a shows the confusion matrix obtained by training the model using the B bridge dataset. Then, we used the data for the other month of B bridge for testing. From Figure 5a, we can see that the model has a seriously low recognition rate for normal, drift, and local gain data, compared to noise, missing, and outlier data. The model cannot identify the above three categories of data effectively. The recognition accuracy of the model in CNN has a certain relationship with the image data. Theoretically, the model learns the features of the data more fully with more images, and the higher the recognition accuracy of the model for the category when the test is finally conducted. The reason for the low recognition accuracy of the local gain category data in Figure 5a can be attributed to the low amount of training data for the above categories and the seriously imbalanced data. To illustrate the effectiveness of data migration for comparison and validation, a new dataset was constructed using the A bridge expansion dataset plus the B bridge training dataset and then retrained using the network model. The confusion matrix was then obtained via the same test set of the B bridge. The recognition results are shown in the Accuracy of the model for the six categories of data is 93.1% from Figure 5b, which is good recognition ability. Additionally, normal data, drift, and local gain data recognition Recall improved significantly, 30%, 8%, and 22.5%, respectively. Noise, missing, and outlier data recognition accuracy fluctuates normally with no significant decre  
Figure 4: The accuracy and loss function curves of A bridge: (**a**) The accuracy curve; (**b**) The loss function curve.