## 3 Results
### Testing Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Models** & **Poor\_1** & **Water\_1** & **Loose\_1** & **Loose\_s** & **Macro-Avg Recall** \\ \hline
**AlexNet** & 99.638/99.498/99.582 & 98.463/98.038/98.627 & 97.347/97.954/97.651 & 96.682/96.209/97.630 & 98.033/97.925/98.372 \\ \hline
**InceptionV3** & 99.470/98.02/99.582 & 98.496/98.267/98.561 & 97.726/98.105/96.931 & 96.209/96.209/96.209 & 97.975/97.846/97.821 \\ \hline
**VGG11** & 99.359/99.220/99.443 & 98.365/98.136/98.430 & 97.954/97.613/97.764 & 93.365/96.209/96.209 & 97.261/97.794/97.961 \\ \hline
**VGG13** & 99.136/99.220/99.359 & 98.757/98.921/98.692 & 97.461/97.689/97.196 & 97.461/95.735/95.735 & 97.772/98.921/97.745 \\ \hline
**VGG16** & 99.443/99.554/99.275 & 98.823/98.398/98.692 & 96.931/97.726/97.461 & 95.735/94.787/95.735 & 97.733/97.616/97.791 \\ \hline
**VGG19** & 99.470/99.275/99.721 & 99.019/99.189/98.561 & 96.931/99.400/97.082 & 96.209/97.976/96.209 & 97.077/97.411/97.893 \\ \hline
**ResNet18** & 99.443/99.493/98.582 & 98.886/98.594/98.627 & 96.779/97.461/97.272 & 96.209/95.261/97.156 & 97.830/97.690/98.159 \\ \hline
**ResNet34** & 99.526/99.498/99.470 & 93.436/98.823/98.234 & 96.211/95.052/97.301 & 95.261/95.735/94.787 & 97.586/95.672/95.470 \\ \hline
**ResNet50** & 99.359/99.638/99.415 & 97.907/98.561/98.234 & 97.954/97.537/97.423 & 98.578/95.261/96.209 & 97.857/97.749/97.820 \\ \hline
**ResNet101** & 99.220/99.498/99.693 & 98.300/99.019/98.300 & 97.878/96.324/97.499 & 96.209/96.682/94.787 & 98.00/98.891/97.570 \\ \hline
**DenseNet121** & 99.415/99.498/99.359 & 98.627/98.332/98.725 & 97.499/97.575/97.689 & 95.261/96.209/95.735 & 97.700/97.903/97.877 \\ \hline
**DenseNet169** & 99.359/99.638/99.470 & 98.627/97.93/98.267 & 96.703/97.272/97.499 & 94.787/94.787/95.735 & 97.369/97.431/97.743 \\ \hline
**MobileNetV2** & 99.638/99.554/99.415 & 98.463/97.842/98.561 & 96.628/97.802/97.726 & 96.209/95.735/94.787 & 97.734/97.733/97.622 \\ \hline
**MobileNetV3** & 99.693/99.387/99.638 & 98.528/98.888/97.711 & 96.741/96.286/97.575 & 97.63/96.209/95.261 & 98.148/97.693/97.546 \\ \hline
**EfficientNetB2** & 99.443/99.498/99.192 & 99.463/97.776/98.103 & 97.234/97.613/97.347 & 96.209/96.209/94.787 & 97.691/97.774/97.357 \\ \hline
**EfficientNetB3** & 99.247/99.359/99.164 & 98.463/98.496/98.234 & 97.423/96.969/97.423 & 95.735/96.682/95.261 & 97.717/97.876/97.520 \\ \hline
**SqueezeNet\_1** & 99.192/99.582/99.415 & 98.071/98.528/98.234 & 97.878/97.120/98.067 & 95.735/95.735/95.261 & 97.719/97.741/97.744 \\ \hline
**SqueezeNet\_1** & 99.331/99.526/99.582 & 98.921/98.463/98.267 & 97.272/97.461/97.726 & 94.313/94.787/96.209 & 97.459/97.559/97.946 \\ \hline \end{tabular}
\end{table} 
Table 4: Recall and macro-average recall of the prediction results of the 18 network models on the test set. The values in each column of the table are listed in ascending order of batch size (\(16/32/64\)). The top five more accurate predictions are labeled in green, while the top five less accurate predictions are labeled in orange. All values in the table are expressed as percentages. \begin{table}
\begin{tabular}{c c c c c} \hline
**Models** & **Poor\_1** & **Water\_1** & **Loose\_1** & **Loose\_s** & **Macro-Avg Precision** \\ \hline \hline
**AlexNet** & 99.085/99.139/99.250 & 98.721/98.977/98.691 & 97.904/97.437/98.284 & 95.327/95.305/94.495 & 97.759/97.7714/97.680 \\ \hline
**InceptionV3** & 99.056/99.38/98.784 & 98.819/98.914/98.400 & 97.912/96.713/98.083 & 96.209/94.860/97.596 & 97.999/97.467/98.216 \\ \hline
**VGG11** & 99.359/99.220/99.304 & 98.591/99.490/98.592 & 97  
For recall, the VGG13_32 model performed the best overall, with a macro-average recall of 98.921%, while the VGG19_64 model had the worst overall performance, with a macro-average recall of only 97.261%. For the interlayer debonding category of distress, the VGG19_64 model had the highest recall at 99.721%, while the InceptionV3_32 model had the lowest recall at 98.802%.