# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 3 Results
### Research on a Road Internal Void Image Enhancement Method Based on Improved Unet Model
#### 3.2.4 Analysis of Image Enhancement Effects Based on the MHUnet Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image Type** & _PSNR_ **(dB)** & _SSIM_ & _LPIPS_ & _NIQE_ \\ \hline Not enhanced & 28.21 & 0.9137 & 0.0253 & 12.1723 \\ Unet & 30.12 & 0.9309 & 0.0211 & 10.8357 \\ NAFNet & 32.38 & 0.9488 & 0.0197 & 10.2675 \\ Uformer & 34.06 & 0.9364 & 0.0145 & 9.3738 \\ Enhanced by MHUnet & 34.65 & 0.9695 & 0.0165 & 9.6543 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model performance metrics for different data types. Figure 12: Comparison of original image quality before and after enhancement. visual quality and image structure. After applying the MHUnet method, the _PSNR_ and _SSIM_ values increased from 28.21 and 0.9137 to 34.65 and 0.9695, respectively. This increase indicates effective suppression of image noise and overall improvement in image quality, bringing the enhanced image nearer to the high-quality reference in aspects like brightness, contrast, and structural details. Additionally, the preservation of local features and textural elements in the image is well-maintained. The _LPIPS_ and _NIQE_ scores decreased from 0.0253 and 12.1723 to 0.0165 and 9.6543, respectively. This reduction shows that the image enhancement method decreases the perceptual differences between the quality-degraded images and the reference images, which helps to improve the accuracy of identifying voids in road interiors where high resolution is crucial. Notably, _LPIPS_ is a metric calculated using deep learning methods that can recognize the deep features of void images and is particularly sensitive to complex textures and shapes. Therefore, the enhanced image quality in this method aids in the accurate identification of void areas. On the other hand, compared to the enhancement results of three models--Unet, NAFNet, and Uformer--the MHUnet model demonstrates superior performance across all four metrics. Although the NAFNet model performs excellently in conventional image processing, it is not entirely suitable for enhancing GPR images of road internal voids. This is mainly because GPR images contain specialized geological structural information, which differs significantly from the features of natural images. Similarly, the Uformer model is not fully applicable to enhancing GPR images of road internal voids. This is primarily because GPR images are formed based on electromagnetic wave reflection signals, which are fundamentally different from natural lighting scenes. This further validates the effectiveness and advancement of the method proposed in this research. This is attributed to the proposed MHUnet model, which references the Unet model architecture capable of reconstructing images at various scales, enhancing image brightness, clarity, and contrast. The inclusion of MHSA and MHCA in the model adjusts and optimizes perceptual image features, improving visual quality and consistency in image perception. Through comprehensive feature learning and multi-level information integration, the model can produce results that are statistically closer to high-quality reference images, thereby enhancing the accuracy and reliability of intelligent void area recognition.