## Experiments
### Comparison and Analysis of Experimental Results
#### 4.3.2 Comparison of Classification Results
The comparative analysis of the classification and identification results of the damage categories of the pipe's inner wall under different classifiers by the three evaluation indexes above is shown in Table 4.  
From the experimental results in Tables 3 and 4, by comparing the recognition and classification results of the Alex classification model, VGG classification model, Lian-VGG classification model, SE-VGG classification model, ResNet50 model, DenseNet121 model, and the classification model proposed in this paper, it can be seen that the classification model optimized in this paper has a better classification effect on normal, lightly corroded, and face-corroded pipes. The classification effect on point-like corrosion is less than that of the optimal algorithm. The difference between the algorithm and the optimal algorithm is \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Corrosion Category** & **Classification Models** & **Precision** & **Recall** & **Specificity** \\ \hline \multirow{8}{*}{Normal pipeline} & Alex & 88.636\% & 90.700\% & 96.031\% \\  & VGG & 90.867\% & 92.561\% & 96.825\% \\  & Lian-VGG & 92.990\% & 92.561\% & 97.612\% \\  & SE-VGG & 92.627\% & 93.492\% & 97.464\% \\  & ResNet50 & 91.402\% & 93.953\% & 96.984\% \\  & DenseNet121 & 91.743\% & 93.023\% & 97.142\% \\  & Algorithms in this paper & **94.037\%** & **95.354\%** & **97.936\%** \\ \hline \multirow{8}{*}{Slight corrosion} & Alex & 90.865\% & 90.001\% & 97.007\% \\  & VGG & 95.544\% & 91.903\% & 98.582\% \\  & Lian-VGG & 96.078\% & 93.334\% & 98.740\% \\  & SE-VGG & 98.035\% & 95.242\% & 99.371\% \\  & ResNet50 & 97.014\% & 92.857\% & 99.063\% \\  & DenseNet121 & 94.660\% & 92.847\% & 98.307\% \\  & Algorithms in this paper & **99.502\%** & **95.46\%** & **99.842\%** \\ \hline \multirow{8}{*}{Pitting corrosion} & Alex & 88.785\% & 92.381\% & 96.220\% \\  & VGG & 90.278\% & 92.863\% & 96.692\% \\  & Lian-VGG & 90.654\% & 92.389\% & 96.850\% \\  & SE-VGG & 95.215\% & **94.318\%** & 98.425\% \\  & ResNet50 & 93.689\% & 91.905\% & 98.006\% \\  & DenseNet121 & 93.137\% & 90.476\% & 97.862\% \\  & Algorithms in this paper & **96.116\%** & 94.295\% & **98.740\%** \\ \hline \multirow{8}{*}{Areal corrosion} & Alex & 91.133\% & 88.108\% & 97.165\% \\  & VGG & 92.307\% & 91.436\% & 97.480\% \\ \cline{1-1}  & Lian-VGG & 92.488\% & 93.813\% & **97.480\%** \\ \cline{1-1}  & SE-VGG & **92.592\%** & 95.246\% & 97.637\% \\ \cline{1-1}  & ResNet50 & 91.705\% & 94.761\% & 97.166\% \\ \cline{1-1}  & DenseNet121 & 90.323\% & 93.334\% & 96.692\% \\ \cline{1-1}  & Algorithms in this paper & 91.8188\% & **96.192\%** & 97.165\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of test classification results for the pipe corrosion sample set. relatively small. At the same time, by comparing the accuracy, recall, and specificity of the five models, we can see that the index values of the proposed model are mostly higher than those of the other six classification models. Therefore, the improved classification model of this paper has a good effect and offers practical classification. In summary, the proposed model can provide technical support for pipeline-damage detection.