# 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
#### 3.2.3 Image Enhancement Model Design Based on an Improved Unet Model--MHUnet  
3D GPR captures road interior void images that often suffer from inconsistent quality. Moreover, void areas feature diverse scales and complex, variable backgrounds. Consequently, an image enhancement network model for void images needs robust feature extraction capabilities. This model must accurately identify and analyze both global and detailed features of void areas across different scales of receptive fields, while also balancing the extraction of local features with the integration of global information, adapting to complex and variable image background conditions.  
Although the Unet network model attempts to expand the receptive field through its multi-layer structure, each layer's receptive field is limited, making it challenging to cover global information throughout the image. The structure's skip connections help maintain local feature information, but they struggle with processing complex void images over large areas. Furthermore, the Unet model inadequately addresses image quality variations caused by noise and signal attenuation in diverse underground media, leading to inconsistent image reconstruction quality.  
Therefore, this section introduces MHSA and MHCA mechanisms into the Unet network model structure. MHSA, by interacting with other units, can acquire rich global information. Simultaneously, by learning long-range dependencies across the entire input image, it effectively expands the model's receptive field, enhancing the handling of large-scale, multiple voids, and complex images. MHCA effectively merges information between different network layers and modules, focusing on different feature combinations to improve the feature extraction performance of void areas and improve image quality enhancement effects.  
The structure of the designed void image enhancement model is illustrated in Figure 10.  
The initial part of the model is the StemConv module, which comprises four convolutional layers (Conv), batch normalization layers (BN), and GeLU activation functions. This module primarily serves to extract preliminary features from the image and enhance non-linear processing capabilities. The DownConv module reduces the spatial dimensions of the feature map through maximum pooling and deepens feature extraction with two convolutional passes to capture abstract features. The MHSA module enhances the capture of global dependency features, enhancing the model's capability to identify key features across the entire image. The MHCA module integrates features from different levels to enhance feature expressiveness. The UpConv module is used to gradually reconstruct the spatial resolution of the features, providing a foundation for high-quality image reconstruction. Concat helps recover more detailed information in void areas. TConv uses two different sizes of convolutional kernels to further process and optimize the feature map, maintaining contextual information while adjusting the number of channels, proving helpful in modifying the dimensions and depth of the feature map.