# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 2 Materials and Methods
### Echo Characteristics of Internal Voids in Roads  
The echo characteristics of voids in roads affect the accuracy of identifying void areas. Therefore, this section will conduct research on the echo characteristics of internal voids in roads. An example of an internal road void appears in Figure 2.  
Figure 1: The on-site inspection images.  
When electromagnetic waves propagate through the interior of a road and encounter a void area, the significant difference in dielectric properties between air and road materials results in a pronounced enhancement of the reflected electromagnetic signal. This is displayed in the image as a prominent bright area. Additionally, when electromagnetic waves encounter these voids, their propagation speed changes, which affects the continuity of the co-axial lines in the image. As the GPR is mobile during detection, the path of reflection is at its minimum when positioned directly over a void. As the radar distances itself from the void area, the path of reflection progressively extends, appearing in the radar image as a hyperbolic shape, creating unique wing-shaped patterns flanking the hyperbolic shape within the image.  
The GPR image characteristics vary with different void formation causes and void dimensions. Three void images with different causes and dimensions were selected for comparison, as shown in Figure 3. For Type 1, the void has dimensions of length 1.4 m, width 1.1 m, burial depth 0.27 m, and net height 0.31 m. For Type 2, the void dimensions are length 1.3 m, width 1.2 m, burial depth 0.51 m, and net height 0.28 m. For Type 3, the void dimensions are length 2.8 m, width 2.3 m, burial depth 0.65 m, and net height 0.51 m. Type 1 and 2 voids were caused by pipeline damage, while Type 3 voids were caused by nearby construction activities.  
Comparison of these three void images reveals that larger-sized voids produce wider reflection ranges with more pronounced extensibility in the images. Greater net height generates stronger reflection signals, while increased burial depth weakens signal strength, resulting in relatively blurred reflection features. Additionally, voids with different causes exhibit distinct characteristics. Pipeline damage-induced voids show localized strong reflections with clear hyperbolic features, well-defined boundaries, and relatively concentrated spatial distribution. In contrast, construction-induced voids display broader reflection ranges, more expanded hyperbolic features, more complex internal structures, and more dispersed boundary reflections.  
During the data annotation process, based on clearly defined void image characteristics, we implemented a three-level expert annotation system. This included initial annotation by technicians, secondary review by technical staff, and final verification by inspection personnel. For cases with disputes, team discussions were mandatory to reach consensus, ensuring consistency and standardization in the annotation process. Addition  
Figure 3: Three types of void images. The red boxed area indicates the void area.  
Figure 2: Image of an internal road void. The red boxed area indicates the void area.  
ally, we established a problem case database to record and analyze challenging annotation cases, providing references and accumulated experience for improving annotation quality.