## 4 Analysis and Discussion
### Testing Phase  
All models were evaluated for performance on an unseen test set containing 9496 distress samples, of which 2639 are interlayer loosening, 211 are structural loosening, 3588 are interlayer loosening, and 3058 are interlayer water seepage. The evaluation of the models included several common metrics such as precision, recall, and f1-score, as well as macro-average precision, macro-average recall, macro-average f1-score, and confusion matrices. In this paper, the f1-score and the macro-average f1-score are comprehensive evaluation metrics because they provide a balance between precision and recall. The evaluation results indicate that all models demonstrate strong recognition capabilities for all four categories of highway distresses. To a certain extent, this result can also show that the features of the distresses in the highway distress images in the region where the data were collected are indeed not as rich as those in the natural images. In the evaluation, the best model was the lightweight network SqueezeNet1_1 trained at a batch size of 64. However, compared to VGG13, which is the best non-lightweight network in the evaluation, SqueezeNet1_1 took longer to converge. Nevertheless, due to its small number of parameters, computation, and size, SqueezeNet1_1 may be more advantageous in real engineering scenarios with limited computational resources. The qualitative analysis of distress samples in each category revealed that interlayer debonding was the most readily identifiable distress, followed by interlayer water seepage and, finally, structural loosening. The main reason for this result is the imbalance in thenumber of samples of different distress categories. In the dataset used, the distress category with the highest number is interlayer debonding, followed by interlayer water seepage, and finally, structural loosening, which accounted for only 6% of the number of samples in interlayer debonding. Therefore, this qualitative analysis result was expected. However, a quantitative analysis shows that the differences in the evaluation metrics for the different categories are not significant, particularly for structural loosening. The visualization plot of the PCA dimensionality reduction of the dataset explains this result. As shown in Figure 4, there was a slight overlap between samples of interlayer loosening and other categories, but the distribution of samples for each distress category exhibited intra-class concentration and inter-class dispersion, particularly in the loose structure. It also explains why the category of structural loosening, which has very small samples, was still well identified in all models. Comprehensively considering the evaluation metrics, it can also be found that some of the models have higher precision but lower recall, such as ResNet34_16 and VGG11_16. Some models have lower precision but higher recall, such as MobileNetV3_16. Combined with the confusion matrix, the models with higher precision but lower recall tend to predict the samples from other categories as the current category; at the same time, they also miss some samples from the current category. The models with lower precision but higher recall tend to predict samples as the current category but may also misclassify some samples from other categories as the current category. The main reasons for these results are the imbalance in the number of samples of the four categories of distress and the inconsistency in the degree of difficulty in recognizing them. A typical example is MobileNetV3_16, which has the lowest macro-average precision, relatively high macro-average recall, and the lowest macro-average f1-score of all the models. In Figure 8, MobileNetV3_16 has the highest number of correct identifications of structural loosening and interlayer debonding compared to other models. However, it also has the highest number of misclassified samples from other categories, such as these two categories.