## 1 Introduction  
The construction of highways is significant for the comfort of travelers, the improvement of transportation efficiency, and the promotion of economic development. To ensure traffic safety, extend the service life of highways, and reduce maintenance costs, it is necessary to detect both overt and hidden distresses of highways in a timely manner. Various methods, including manual inspection, core drill sampling, and non-destructive testing, have been implemented for the detection of highway distress. Manual inspection is effective for detecting surface distress, but it can be costly. Destructive detection methods, such as core drilling, have been gradually replaced due to their destructiveness and inefficiency. At present, as a primary method to detect highway hidden distresses, non-destructive detection technology mainly uses acoustic waves, electromagnetic waves, and infrared radiation to quickly and accurately identify highway distresses and provide a scientific basis for maintenance work. As a non-destructive testing technique, ground-penetrating radar (GPR) has been widely used to detect highway hidden distresses due to its high efficiency, continuous acquisition, and relatively low cost, and this technique has proven to be effective in the identification of subsurface defects and anomalies [1]. To detect GPR-based highway hidden distresses, the primary research is focused on the analysis and interpretation of GPR data. GPR data is divided into three types: one-trace waveforms (A-scan), two-dimensional profiles (B-scan), and three-dimensional data (C-scan). [PERSON] et al. conducted extensive experiments based on GPR A-scan data on asphalt concrete pavements using a support vector machine approach, and the results demonstrated its effectiveness in pavement debonding detection [2]. This analysis method based on A-scan data is simple and effective, but it is easily affected by partial noise and lacks comprehensive consideration of neighboring signals. [PERSON] et al. used advanced air-coupled three-dimensional ground-penetrating radar (3D-GPR) to detect highway damage, and the results showed a strong correlation between the abnormal signals of the 3D-GPR images and pavement damage, verifying the ability of 3D-GPR to accurately identify highway damage features [3]. However, the analysis and interpretation of 3D data face challenges, such as the management of a large amount of data, the need for high computing power, and difficulty in labeling the 3D data. At present, the analysis and interpretation of GPR data still focus on two-dimensional GPR B-scan data, including a series of successive A-scans as the transceiver antenna moves equidistantly in a given direction. These data reflect the longitudinal section of the highway and have a smaller computational volume compared to the C-scan. Due to various factors, such as the noise of each hardware module of the instrument, the inhomogeneity of the underground medium, and the interaction of echoes, there will still be some noise in the GPR B-scan data that affects recognition and detection accuracy. However, research based on GPR B-scan data has rapidly developed with the progress of various digital signal processing techniques for noise suppression, such as Kalman filtering, principal component analysis, and wavelet transform [4; 5]. At present, various classification methods are proposed to identify highway distresses based on GPR B-scan data, which can be divided into three groups based on different feature extraction methods: traditional-based, machine learning-based, and deep learning-based. These methods are aimed at extracting essential features from GPR B-scan data to achieve accurate classification. Traditional feature extraction methods, such as edge detection, least squares, and Hough transform, and machine learning-based methods, such as support vector machine (SVM), artificial neural network, and naive Bayes classifier, rely heavily on a priori knowledge and expert experience, which is a significant problem that cannot be avoided.