## 4 Conclusions  
In order to precisely classify highway interlayer distresses, a dataset was established based on GPR B-scan images of highways in Jiangsu Province, China. The dataset is divided into a training set, validation set, and test set, in which the training set contains 19,222 B-scan images, the validation set contains 3203 B-scan images, and the test set contains 9613 B-scan images. To evaluate the performance of various models, several metrics were used. The losses calculated on the training and validation set and the learning curves were produced to evaluate the training performance. The F1 score, macro-F1 score, accuracy, and confusion matrix were used to evaluate the model's performance on the test  
Figure 7: The Grad-CAM results of models.  
set. Furthermore, to gain a more comprehensive understanding of the model's learning process and predictive mechanisms, the Grad-CAM technique was employed to generate heatmaps illustrating the specific regions within the images that had the most significant impact on the model predictions. Through the above work, the conclusions of this paper are described as follows.  
Firstly, in the performance evaluation of the model, we should not only consider the test accuracy, but also analyze the loss curves in the training stage, especially in the case of little difference in test accuracy, so as to judge the generalization performance of the model. Secondly, all models have similar test accuracy, but ResNet-4, ResNet-6, SqueezeNet1_0, and ResNet-8 exhibit no overfitting which means they have a good generalization performance. Finally, the generalization performance of a model is not merely associated with the number of parameters within the model, but also with the architecture of the model. For example, MobileNet V2 and EfficientNet B0 are more serious than ResNet-10 and ResNet-14 in overfitting, although MobileNet V2 and EfficientNet B0 have fewer parameters.  
This paper provides satisfactory answers on the classification of interlayer distresses in pavement and lays the groundwork for a more detailed fine-grained interlayer distresses classification. This paper also offers valuable insights into the real-time classification of highway interlayer distresses, which is crucial for highway maintenance. Meanwhile, ResNet-4, ResNet-6, SqueezeNet1_0, and ResNet-8 may be used as a backbone network candidate for interlayer distress detection as a more challenging downstream task. Additionally, ResNet-4, ResNet-6, SqueezeNet1_0, and ResNet-8 will be tested and evaluated in other hidden distress recognition and detection based on GPR images as future work.  
Not applicable.  
The dataset presented in this article is not readily available because the data are part of an ongoing study. Requests to access the dataset should be directed to contact the corresponding author.  
We sincerely acknowledge the engineering technicians from Jiangsu Sinoroad Engineering Technology Research Institute Co., Ltd. in conducting all the fieldworks and collecting the GPR data.  
Authors [PERSON] and [PERSON] were employed by the company Jiangsu Sinoroad Engineering Technology Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Jiangsu Sinoroad Engineering Technology Research Institute Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.