## 2 Methodology  
### Dataset Construction and Analysis  
The data collection in this paper is mainly concentrated in Jiangsu Province, including Nanjing-Lianyungang Highway, Shanghai-Nanjing Highway, Beijing-Shanghai Highway, Coastal Highway, Nanjing-Hangzhou Highway, Changzhou-Yixing Highway, and Wuxi-Zhangjiagang Highway. The data are collected by using a vehicle-mounted GPR, which emits high-frequency electromagnetic waves to the ground through transmitting antenna. When these waves encounter media with varying dielectric characteristics, they produce reflection and refraction, leading to changes in the intensity and waveform of the electromagnetic field. The reflected waves are then captured by the receiving antenna. By data processing and analysis, it is possible to infer the spatial position, structure, and shape of the underground objects. The GPR used in this paper is the fourth-generation high dynamic ground penetrating radar, i.e., MALA GX750. The basic parameters are shown in Table 1. The center frequency of the emitted electromagnetic wave is 750 MHz; the scanning rate is 1290 channels per second; the detection depth ranges from 0 to 1.5 m; the coupling mode is air coupling; and the weight is 3.6 kg. In the data collected by GPR, the A-scan records the radar response at a specific position, and the image formed by connecting the A-scans recorded along the measurement line is referred to as a B-scan. The horizontal axis of the B-scan represents spatial position, while the vertical axis represents time. The original data were sequentially processed through dewow, time-zero correction, time-varying gain, background removal, deconvolution, band-pass filtering, and moving average processing to obtain B-scan images with clearly visible distress features. Subsequently, the B-scan images processed were labeled manually. Finally, once distresses are labeled, a corresponding label file is generated, and the distress samples are then cropped according to the coordinate information of the distress box in the label file. The specific process is illustrated in Figure 1.  
The classification of interlayer distresses can improve maintenance efficiency and reduce maintenance costs. For example, if an interlayer distress is identified as interlayer debonding, maintenance is necessary but not urgent under the owner's current regulations. If an interlayer distress is identified as interlayer water seepage, it indicates a problem with horizontal drainage, and it is necessary to not only adopt the grouting maintenance method, but also consider implementing a drainage maintenance scheme. If an interlayer distress is identified as interlayer loosening, then it is necessary to adopt the grouting maintenance scheme in time. Therefore, the classification of interlayer distresses is a very important task for highway operation and maintenance engineering. So, interlayer distresses in pavement need to be further categorized as interlayer debonding, interlayer water seepage, and interlayer loosening. Figure 2 shows the B-scan images and core samples of interlayer distress and samples of three types of interlayer distress. In Figure 2, the red square represents the location of the distress, A represents the coring position, B represents the interface of the surface layer and the upper base layer, and C represents the interface of the upper base layer and the \begin{table}
\begin{tabular}{c c} \hline
**Technical Specification** & **Parameters** \\ \hline Antenna center frequency & 750 MHz \\ Scanning rate & 1290 channel/s, time window 75 ns \\ Depth of detection & 0–1.5 m \\ Coupling mode & Air coupling \\ Weight & 3.6 kg \\ \hline \end{tabular}
\end{table} 
Table 1: Technical specifications of GX750. Figure 1: The establishment process of distress dataset. lower base layer.