# 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  
The MHUnet model was utilized to enhance the quality of low-quality void maps within road interiors, with corresponding results shown in Figure 11.  
As evident from the figure, compared to the low-quality image, the enhanced image exhibits a deepening of black areas and a brightness increase in white and gray areas, significantly improving the image contrast. The enhanced contrast makes the distinction between void areas and the surrounding background more pronounced. The increase in brightness aids in better recognition of details within the image. In the enhanced image, the edges of the internal structure areas of the road are sharper and clearer, which helps in further distinguishing between void and non-void areas. In summary, the enhanced image has a higher degree of detail recognition, which helps to improve the accuracy and reliability of the interpretation of the void map. The MHUnet model was used to enhance the quality of collected GPR images, with the comparison between pre- and post-enhancement shown in Figure 12.  
Figure 11: Before and after comparison of image quality enhancement based on the MHUnet model. Figure 10: Image enhancement model structure. As can be seen from the figure, compared to the original collected images, the enhanced images show improvements in multiple aspects. The void area boundaries are sharper, detail features are more distinguishable, and overall image blur is reduced; the contrast between void areas and background is more pronounced with richer grayscale levels; background noise is effectively suppressed while main features are more prominent; additionally, the hyperbolic features of void areas are more distinct, structural boundaries are clearer, and target areas show better differentiation from surrounding environments. These improvements contribute to higher accuracy in subsequent intelligent recognition. To avoid having two different feature distributions (original and enhanced) in the dataset and reduce the negative impacts of data distribution differences on model training, the image enhancement method was applied to all 1700 images in the dataset. Additionally, this approach significantly simplifies the processing workflow by eliminating the need for image quality judgment and classification. To demonstrate the advancement of our proposed method, comparison with existing image enhancement models is necessary. Among existing image enhancement models, NAFNet and Uformer are widely applied. NAFNet has extensive applications in image processing, primarily in low-light image enhancement, underwater image enhancement, and remote sensing image denoising. This method shows excellent performance in motion blur removal, Gaussian noise removal, and image quality improvement, with advantages in computational efficiency and small model size [44; 45; 46]. The Uformer method is mainly applied in low-light image enhancement, particularly excelling in ultra-high-definition image processing. This method not only improves image quality but also serves as a preprocessing step for downstream visual tasks such as face detection [47; 48; 49]. The image enhancement effect was quantitatively analyzed using four indicators: _PSNR_, _SSIM_, _LPIPS_, and _NIQE_. Among them, the quality-degraded image and the MHUnet method enhanced image corresponding indicators were calculated using the image preprocessed image as the reference image, as shown in Table 1.  
The table reveals that the quality-degraded images have lower _PSNR_ and _SSIM_ values, and higher _LPIPS_ and _NIQE_ scores, indicating a certain similarity in image structure between the low-quality images and reference images, but still a significant difference in