# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 5 Conclusions  
This study proposes an intelligent recognition method for road internal voids based on improved image enhancement technology. Three-dimensional GPR detection was conducted in Shenzhen to establish a void image dataset. The proposed MHUnet model incorporates MHSA and MHCA mechanisms, achieving significant improvements in image brightness, clarity, and contrast, enhancing perceptual consistency, and matching statistical distributions with high-quality reference images. For the enhanced images,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Number of Voids** & **Accuracy (\%)** \\ \hline Model detection & 10 & 90 \\ Accurate verification & 9 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Verification of model detection accuracy.  
the quantitative indicators improved significantly: \(PSNR\) increased by 6.44 dB, \(SSIM\) by 0.0558, \(LPIPS\) by 0.0088, and \(NIQE\) by 2.518. Experimental validation demonstrates that the enhanced images provide higher-quality feature information, improving the model's accuracy and sensitivity while exhibiting superior recognition capabilities and generalization performance. Compared to the original images, the improved model showed increased performance metrics: precision by 1.39%, recall by 1.68%, F1 score by 1.55%, and mAP by 0.88%. The research findings provide a new technical solution for the intelligent detection of road internal voids, with significant practical implications for improving the intelligence level of road maintenance.  
The next steps will focus on further optimizing the intelligent recognition model for internal road voids and conducting larger-scale engineering practice verification. Additionally, we will conduct quantitative assessment research based on void images to evaluate void size, depth, deterioration degree, void grade, and impact on road damage. By establishing a comprehensive void assessment system, we aim to better guide road maintenance work prioritization, improve maintenance efficiency, and provide important technical support for road lifecycle management, thereby enhancing the engineering application value of the research.  
Conceptualization, Q.K. and X.L.; Data curation, Q.K. and A.M.; Formal analysis, A.M. and L.Y.; Funding acquisition, Q.K. and A.M.; Investigation, Q.K. and X.L.; Methodology, Q.K. and X.L.; Project administration, X.L. and A.M.; Resources, X.L. and A.M.; Software, A.M. and L.Y.; Supervision, X.L.; Validation, A.M. and L.Y.; Visualization, A.M. and L.Y.; Writing--original draft, Q.K. and X.L.; Writing--review and editing, A.M. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program, grant number 2022 YFB2602100. This project was sponsored by the Ministry of Science and Technology of China.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality agreements with government administrative departments and partner institutions who jointly collected the ground-penetrating radar data. The authors require prior authorization from these collaborating parties before sharing the data.  
Author [PERSON], [PERSON], [PERSON] was employed by the company Shenzhen Urban Transport Planning Center Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.