## 4 Results and Analysis
### Optimization Results of Vehicle Detection Robustness  
To verify this improvement, we conducted comparative experiments on vehicle detection before and after the restoration of degraded traffic images. The evaluation employed precision, recall, and mAP (with an IoU threshold of 0.5) as metrics, tested on 76 real dense fog traffic images from DAWN. The qualitative results of vehicle detection before and after restoration are shown in Figure 21, while the quantitative results are presented in Table 12.  
Figure 20: Restoration effects comparison of real traffic dense fog images under different models. Figure 21 clearly demonstrates that TDIRM can effectively remove fog interference, particularly for large target vehicles in front. The model also has certain restoration capabilities for small distant vehicles that were severely obscured by fog, significantly improving their detection probability and successfully detecting vehicles that were previously missed due to blurred features. Moreover, images restored by TDIRM maintain high fidelity without arbitrarily altering the traffic elements present in the original degraded images, ensuring strong consistency between input and output. The model effectively recovers color and texture information in traffic scenes, thereby enhancing overall image quality. As shown in Table 13, after processing through TDIRM, the mAP of vehicle detection in real foggy images increased by 6.92%, with both precision and recall rates improving by over 4%. These results prove that real foggy images restored by TDIRM significantly improve vehicle detection accuracy while reducing the false detection rate. Additionally, confidence levels of detected vehicles all showed some degree of improvement. This indicates that TDIRM can effectively enhance the robustness of vehicle detection in adverse traffic environments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Type** & **mAP50 (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline Real traffic foggy images & 78.62 & 75.60 & 87.15 \\ The restored images & 85.54 & 83.18 & 92.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Quantitative comparison of vehicle detection results before and after recovery in real traffic dense fog images. Figure 21: Qualitative comparison of vehicle detection results before and after recovery in real traffic dense fog images. As shown in Table 14, ablation experiments were conducted for ISE and TCE to validate the effectiveness of different components in TDIRM. CLIP IE refers to the pretrained CLIP image encoder, while SelfAtt denotes the self-attention module. The result demonstrates that replacing the CLIP image encoder with the ISE significantly improves both PSNR and SSIM metrics. This indicates that ISE can effectively reduce the impact of degradation information during the semantic extraction process, thereby minimizing their interference with the SDM reverse sampling procedure and ensuring better consistency and fidelity in restored images. Furthermore, compared to SelfAtt, TCEAtt more effectively integrates different types of control embedding during the U-Net reverse denoising process. This enhanced integration capability leads to superior preservation of image fidelity in final restoration results. Table 14 demonstrates the effectiveness of different condition controls in TDIRM. The degraded image embedding DIE serves as an essential component since it represents the original input control condition, while both semantics embedding SE and segmentation embedding SegE function as optional controls. SE encapsulates comprehensive semantic information, including structure, objects, and layout within the entire image, whereas SegE specifically contains all critical traffic element information. The result reveals that combining SE with DIE achieves approximately 3 dB PSNR improvement over using DIE alone, along with significant enhancements in both SSIM and NIQE.