# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 1 Introduction  
The formation of internal road voids is primarily influenced by natural geological conditions, engineering factors, and environmental effects, specifically including groundwater loss, construction quality, drainage systems, vehicle loads, underground pipeline leakage, and environmental changes. These factors collectively lead to the loss of subgrade materials and structural damage, significantly affecting road structure stability, reducing load-bearing capacity, and potentially causing road collapse under the stress of heavy vehicles. Additionally, if internal road voids are not repaired in a timely manner, they will continue to develop and expand under the influence of vehicle loads, increasing the cost of road maintenance and affecting traffic efficiency during the repair process [1, 2, 3]. To ensure the health of road operations and traffic safety, scholars have conducted research on detection methods for concealed void-related diseases within roads. By accurately understanding the distribution and development of voids, repair strategies can be formulated to minimize their impact on traffic. At the same time, numerous intelligent recognition algorithms have been proposed, enhancing the efficiency of internal road void detection [4, 5]. However, due to factors such as noise interference and the complexity of the medium materials, it is challenging to maintain high-quality internal road image data, affecting the accuracy ofvoid area discrimination [6; 7; 8]. Therefore, researching image enhancement methods for low-quality internal road void image data can improve the accuracy of void identification. Compared to methods for detecting apparent road diseases, the internal detection cannot be directly assessed through visual observation. The detection includes invasive methods such as slot cutting [9; 10; 11], as well as non-invasive techniques such as acoustic testing and electromagnetic wave detection [12; 13; 14]. Compared to destructive testing, non-destructive testing does not compromise the integrity of the road structure, allowing for a comprehensive assessment of the internal structure of the road and ensuring the continuity of operations. Among non-destructive methods, GPR is widely used. This method analyzes the internal state of roads by interpreting electromagnetic wave signals. Compared to other methods, GPR features strong penetration, high resolution, and fast data collection. Moreover, the detection process does not require contact with the surface of the object, enabling the rapid, comprehensive, and safe detection of internal road voids. Therefore, scholars often use GPR to conduct research on road defects. GPR signal processing significantly impacts data accuracy and interpretation speed. Researchers have achieved road internal structure assessment through Neyman-Pearson spatial correlation analysis [15; 16] and clarified radar signal attenuation characteristics and penetration depth [17]. To improve signal quality, methods including two-dimensional digital filter clutter removal, fast independent component analysis with multifractal spectrum denoising [18; 19], and parameter-optimized generalized S-transform [20] were proposed. These studies enhanced adaptability in complex environments by reducing noise and strengthening signal features. Research has shown that void areas appear as bright features in radar images, with high-frequency components closely related to details and edge variations [21; 22; 23]. Analysis of high-frequency image information significantly improves void area interpretation accuracy, providing reliable evidence for internal road anomaly detection. Researchers have conducted intelligent void detection studies using YOLO series models [24; 25; 26; 27], knowledge distillation techniques [28], and 3D convolutional neural networks [29], but have focused mainly on algorithm improvements while neglecting image quality enhancement.