## 6 Conclusions  
In this study, the image features of loose subgrades, cavities, and voids were studied by means of forward simulation, complex signal analysis, and image processing. The SSD and YOLO, which are both deep learning-based models, were used to quickly detect and identify GPR subgrade defects. The main conclusions are as follows:  
1. By using the GPR and forward simulation methods, a layered structure model of a highway with surface, base, and subgrade layers was established. The simulation results were consistent with the theoretical calculations in the verification of the rationality of the forward simulation, confirming the reliability of the model's calculations.
2. Using complex signal analysis techniques and the forward simulation results of the subgrade structure layers, we extracted the instantaneous amplitude, phase, and frequency attributes of the loose subgrade, void, and cavity defects. These attributes helped to analyze the GPR response characteristics of the subgrade defects, improving the boundary judgment of the structural layers and abnormalities in the GPR images.
3. Techniques such as direct wave removal and signal gain enhancement improved the response characteristics of the GPR images of the subgrade defects, enhancing the accuracy of GPR detection and the interpretation of voids.
4. In the PyTorch environment on the vs. Code platform, the SSD and YOLO v3 algorithms were trained and evaluated with a series of metrics, including the _Precision_, _Recall_, _F1_, _AP_ and _mAP_. On the subgrade defect radar image dataset created in this  
Figure 23: Intelligent detection results for on-site roadbed defects.  
Figure 22: Geological radar image.  
study, the YOLO v3 algorithm performed the best, achieving an mAP value of 76.69% and demonstrating higher prediction accuracy.
5. A field test was conducted with the YOLO v3 and SSD object detection algorithms within the intelligent defect identification system. This system was employed to identify and classify defects in radar images, thereby meeting the requirement for rapid defect image recognition. Compared to the SSD, YOLO v3 demonstrated superior performance in the intelligent detection and classification of the defect locations, extents, and types within the radar images of subgrade voids.  
This study primarily investigated subgrade defects through methods such as GPR forward simulation, complex signal analysis techniques, and image processing. These approaches provide a basis for the intelligent identification of highway subgrade defects by characterizing the defect features. Additionally, deep learning-based algorithms for defect detection and classification in highway subgrade GPR images were tested and evaluated. The results offer a technical reference for the non-destructive and intelligent detection of highway subgrade defects. Future work could incorporate parameters such as the relative permittivity, magnetic permeability, conductivity, and other physical properties of the subgrade to simulate real-world conditions more accurately and further enhance detection accuracy.  
Methodology, P.L. and C.L.; Software, P.L. and X.L.; Validation, P.L., M.B. and X.L.; Resources, M.B.; Writing--original draft, P.L.; Writing--review & editing, P.L.; Supervision, M.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 41672339). The authors express their gratitude for the financial support provided.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.