## 3 Results
### Testing Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Poor\_1** & **Water\_1** & **Loose\_1** & **Loose\_s** & **Macro-Avg F1-Score** \\ \hline
**AlexNet** & 99.361/99.318/99.416 & 98.592/98.505/98.659 & 97.625/97.695/97.966 & 96.00/95.755/96.037 & 97.894/97.818/98.019 \\ \hline
**InceptionV3** & 99.263/99.082/99.181 & 98.657/98.589/98.481 & 97.819/97.404/97.503 & 96.209/95.529/96.897 & 97.987/97.654/98.016 \\ \hline
**VGG11** & 99.359/99.220/99.373 & 98.478/98.313/98.511 & 97.639/97.391/97.764 & 95.631/96.437/96.209 & 97.777/97.840/97.964 \\ \hline
**VGG13** & 99.205/99.344/99.290 & 98.484/98.663/98.386 & 97.572/97.744/97.529 & 97.115/96.651/97.115 & 98.094/98.100/98.080 \\ \hline
**VGG16** & 99.277/99.346/99.331 & 98.436/98.462/98.402 & 97.559/97.819/97.628 & 96.190/96.154/96.882 & 97.866/97.945/98.061 \\ \hline
**VGG19** & 99.360/99.220/99.279 & 98.584/98.298/98.577 & 97.615/97.378/97.674 & 95.755/96.154/95.981 & 97.828/97.763/97.878 \\ \hline
**ResNet18** & 99.290/99.360/99.388 & 98.502/98.465/98.546 & 97.462/97.628/97.716 & 95.755/96.403/96.019 & 97.752/97.964/97.917 \\ \hline
**ResNet34** & 99.332/99.194/99.235 & 98.445/98.420/98.298 & 97.466/97.345/97.402 & 95.714/96.651/96.618 & 97.739/97.905/97.889 \\ \hline
**ResNet50** & 99.262/99.339/99.207 & 98.454/98.69/89.379 & 97.510/97.799/97.516 & 95.529/95.714/96.437 & 96.689/97.874/97.885 \\ \hline
**ResNet101** & 99.303/99.263/99.396 & 98.477/98.376/97.487 & 97.693/97.507/97.703 & 95.105/96.154 & 97.644/97.563/97.910 \\ \hline
**DenseNet121** & 99.373/99.332/99.248 & 98.514/98.429/98.725 & 97.592/97.649/98.700 & 96.403/96.667/96.190 & 97.970/98.019/97.991 \\ \hline
**DenseNet169** & 99.124/99.168/99.249 & 98.289/98.375/98.492 & 97.275/97.419/97.481 & 96.386/95.923/96.420 & 97.768/97.721/97.911 \\ \hline
**MobileNetV2** & 99.154/99.374/99.304 & 98.399/98.276/98.545 & 97.347/97.543/97.782 & 96.209/95.735/96.154 & 97.777/97.732/97.946 \\ \hline
**MobileNetV3** & 99.223/99.152/99.182 & 98.512/98.294/98.241 & 97.629/97.338/97.482 & 94.713/95.529/96.403 & 97.519/97.578/97.827 \\ \hline
**EfficientNetB2** & 99.332/99.139/99.068 & 98.495/98.323/98.200 & 97.437/97.520/97.292 & 95.082/95.529/96.154 & 97.586/97.628/97.678 \\ \hline
**EfficientNetB3** & 99.331/99.262/99.081 & 98.383/98.287/98.395 & 97.460/97.393/97.276 & 95.059/96.000/96.172 & 97.558/97.735/97.731 \\ \hline
**SqueezeNet1\_0** & 99.178/99.305/99.331 & 98.376/98.416/98.524 & 97.472/97.601/97.771 & 96.651/95.962/96.172 & 97.919/97.821/97.950 \\ \hline
**SqueezeNet1\_1** & 99.234/99.263/99.264 & 98.550/98.495/98.557 & 97.679/97.683/97.763 & 96.135/95.923/96.897 & 97.900/97.841/98.120 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F1-score and macro-average f1-score 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. MobileNetV3_16, EfficientNetB2_64, and DenseNet169_16, which showed relatively poor performance overall. The confusion matrices in Figure 8 show the detailed classification results of the model on the four categories of distress samples from the test set. The prediction results show that each category of distress was misclassified differently when the model made a classification error. The interlayer loosening category of distress was most likely to be predicted as interlayer water seepage, followed by interlayer debonding. The structural loosening category of distress was usually only misclassified as interlayer loosening. The interlayer debonding category of distress was most likely to be predicted as interlayer loosening and usually not be misclassified as structural loosening. The interlayer water seepage category of distress was most likely to be predicted as interlayer loosening, followed by interlayer debonding.