## 5 Conclusions  
This study selected some classical CNN models for training and testing to determine a more applicable and efficient classification model for the characteristics of highway distress data collected in East China, particularly in Jiangsu and Zhejiang provinces. Evaluation metrics, such as precision, recall, and f1-score, were also used to evaluate the model, and the confusion matrix and Grad-CAM technique were also used to analyze the model to provide ideas for further research. Four main categories of distress are present on highways in the region: interlayer debonding, interlayer loosening, interlayer water seepage, and structural loosening. The data were labeled by professionals and produced into a dataset. This dataset shows that the dataset in this region is mainly characterized by sample imbalance, no obvious hyperbolic features, similar features of interlayer distresses, and relatively simple features. This study aims to find the optimal CNN model for a given dataset by considering both model performance and complexity. Eighteen network models derived from eight different architectures were trained and evaluated, and some conclusions are summarized below. 1. All 18 models perform well. The VGG13 architecture stands out for its excellent classification performance at batch sizes of 16, 32, and 64. For lightweight networks, SqueezeNet1_1 performs particularly well at a batch size of 64;
2. For the identification of the four types of highway hidden distresses, the f1-score of interlayer debonding and interlayer water seepage is the highest, while the f1-score of interlayer loosening and structural loosening is slightly lower due to the similar characteristics of interlayer distress and the limited number of samples, respectively;
3. After a comprehensive analysis of the evaluation results, confusion matrix, and Grad-CAM heat map, this study concludes that the number of layers in a CNN model does not necessarily determine its ability to recognize highway hidden distresses in GPR B-scan images. Networks with fewer layers can achieve similar or even better performance than deeper, more complex networks while requiring fewer resources and being less constrained. Automatic detection of highway hidden distress based on GPR is still a challenging task, and its detection accuracy is affected by a variety of factors. Among these factors, the classification accuracy of the distress samples is one of the most critical factors. Therefore, the findings of this study not only identify a suitable classification model of highway distress in this region but also provide valuable insights for further automated distress detection tasks. In particular, this study provides ample experimental evidence for selecting the backbone network for feature extraction. Moreover, according to China's highway construction standards, highways in different regions have similar structures, and the CNN model has the ability to extract high-level semantic features, so the CNN model proposed in this paper has potential application value in other regions. The future of work can be described as follows. Firstly, the classification model, distress location algorithm, and foreground and background recognition method are combined to establish an accurate and efficient highway distress detection scheme. Secondly, the proposed classification models will be tested and analyzed with highway distress samples from other regions to determine the generalization of these models. Thirdly, using knowledge distillation and other transfer learning methods, the object detection model in natural images can be transferred to the field of highway distress detection. Lastly, distress detection models based on lightweight networks, such as SqueezeNet1_1, may be integrated into GPR acquisition systems for real-time detection and identification. Conceptualization, G.J. and C.L. ; Data curation, Q.L. ; Formal analysis, G.J. and C.L. ; Funding acquisition, G.J. and C.L. ; Investigation, G.J. ; Methodology, Q.L. and W.C.; Project administration, G.J. and Q.L.