## 1 Introduction
However, the rapid development of deep learning has brought breakthroughs, particularly in image feature extraction [6]. Deep learning-based methods can achieve end-to-end recognition and detection, automatically learning feature representations directly from GPR B-scan images [7]. The recognition and detection of highway distress using GPR have always been challenging tasks. [PERSON] et al. systematically reviewed typical cases and studies of GPR for highway and subsurface detection, highlighting the many challenges with highway GPR data [8]. [PERSON] et al. conducted a comprehensive review of a computer vision model and its application to highway hidden distress classification, detection, segmentation, and quantification, demonstrating that in the modern transportation field, researchers and practitioners have been working on the development of methods that can efficiently utilize GPR data for the identification and evaluation of highway hidden distresses [9]. After analyzing and summarizing numerous previous studies, this paper concludes that the three main factors affecting the accuracy of the automatic detection task of highway hidden distresses are the classification accuracy of distress samples, the localization accuracy of distresses, and the recognition accuracy between foreground (distresses) and background. Effective feature extraction and accurate classification of distress samples are crucial for the accuracy of the automatic distress detection task. Previous studies have shown that selecting the appropriate backbone network for extracting features is particularly important for the performance of these detection methods [10]. Numerous studies have demonstrated that deep learning-based image recognition methods, particularly those based on convolutional neural networks (CNNs), have achieved outstanding performance in highway distress recognition with GPR B-scan images. However, it should be noted that current recognition and detection efforts mainly focus on extracting hyperbolic features from GPR B-scan images [11]. Since 2017, this study has conducted highway hidden distress detection work in East China, mainly in Jiangsu and Zhejiang provinces, and collected measured data from several highways with a total length of nearly 1500 km. By combining the GPR B-scan data with the validation results from on-site core drilling sampling, which were verified by experts in the field, the main categories of highway distress in the region were identified, including interlayer debonding, interlayer water seepage, interlayer loosening, and structural loosening. Structural loosening is the most serious and least numerous hidden distress, which is to be expected since China places a high priority on highway maintenance. Interlayer distresses are subdivided due to maintenance cost considerations in actual projects, but their characteristics are largely similar. Identification of distress categories has become more challenging as the differentiation between distress categories has become more refined. Analysis of the measured data revealed that unlike common highway distresses such as cracks [12] and Cavities [13], the four categories of distresses found in East China do not exhibit typical hyperbolic features in the image data. As a result, the accurate detection of these four categories of distress is much more difficult in this region. Several classification models based on CNN architectures have been proposed for large natural image datasets, but GPR B-scan distress image features are relatively simple, lacking the complexity and diversity of natural images [14]. Therefore, for GPR B-scan images of highway hidden distresses in East China, particularly in Jiangsu and Zhejiang provinces, where there is an imbalance in the number of category samples, no clear hyperbolic features of the distresses, and relatively simple image features, it is unclear which CNN classification model is more effective in terms of considering the combination of model performance, parameters, complexity, and sizes.