## Experiments
### Comparison and Analysis of Experimental Results
#### 4.3.2 Comparison of Classification Results  
The image-sample sets were divided into training and test sets and input into the improved classification model in this paper for classification and recognition. Meanwhile, to better demonstrate the recognition capability of the newly constructed classification model for water-supply pipe-corrosion detection, its performance was compared with those of the traditional VGG classifier [26], the traditional Alex model [27], the ResNet50 model [28], the DenseNet121 model [29], the SE-VGG model, and the VGG model using the joint loss function, and the confusion matrix plots of the classification and recognition results are shown in Figures 13-19. Figure 16: SE-VGG classification model. Figure 17: ResNet50 classification model. Figure 18: DenseNet121 classification model. The full name of ROC is receiver operating characteristic. The curve is drawn with the true positive rate (sensitivity) as the ordinate and the false positive rate (1-specificity) as the abscissa. For a classifier, we can get a TPR and FPR point pair according to its performance on test samples. In this way, the classifier can be mapped to a point on the ROC plane. Adjust the threshold value used by this classifier to get a curve passing (0,0), (1,1), which is the ROC curve of this classifier. The AUC value is the area below the ROC curve. Generally, AUC values range from 0.5 to 1.0, and a larger AUC represents better performance. AUC is a standard used to measure  
Figure 23: ROC Curve of DenseNet Model. Figure 24: ROC Curve of ResNet Model. Figure 22: ROC Curve of Lian-VGGNet Model. It can be seen from Figures 20-26 that the ROC curve of the improved model in this paper is closer to the optimal point (0,1). Secondly, the AUC value of the improved model in this paper is significantly higher than that of the other six comparison models, which proves the effectiveness of the model in this paper. The above experiments achieved the identification and classification of image corrosion, following the four different types of pipe-wall-corrosion sample map under other classification models to compare and explain the classification results, as shown in Table 3. The number of normal pipe samples in the test set was 215, the number of lightly corroded pipe samples was 210, the number of pitting corrosion pipe samples was 210, and the number of surface-corrosion-pipe samples was 210. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Corrosion Category** & **Alex** & **VGG** & **Lian-VGG** & **SE-VGG** & **ResNet50** & **DenseNet121** & **Model in This Paper** \\ \hline Normal pipeline & 195 & 199 & 199 & 201 & 202 & 200 & **205** \\ Slight corrosion & 189 & 193 & 196 & 200 & 195 & 195 & **200** \\ Pitting corrosion & 190 & 195 & 194 & 199 & 193 & 190 & 198 \\ Areal corrosion & 185 & 192 & 197 & 200 & 199 & 196 & **202** \\ Total number of correct & 759 & 779 & 786 & 800 & 789 & 781 & **805** \\ Classification accuracy & 89.822\% & 92.189\% & 93.018\% & 94.674\% & 93.372\% & 92.426\% & **95.266\%** \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of test classification results for the four types of pipe damage sample sets. Figure 25: ROC Curve of SE-VGGNet Model. Figure 26: ROC curve of the improved model in this paper. Next, the overall performance of the algorithm was analyzed in terms of its precision, recall, and specificity for several classification models [30], which were calculated as follows. 1. Precision: \[Precision=\frac{TP}{TP+FP}\] (19)
2. Recall: \[Recall=\frac{TP}{TP+FN}\] (20)
3. Specificity: \[Specificity=\frac{TN}{TN+FP}\] (21) where \(TP,FP,TN,FN\) denote true positive, false positive, true negative, and false negative, respectively.