## 5 Discussion and Conclusions  
In this study, we delved into two crucial aspects of vehicle detection: robustness benchmark evaluation and robustness optimization. Fourteen fundamental factors that significantly and frequently affect vehicle detection in real traffic scenarios were designed \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **PSNR** & **SSIM** & **NIQE** \\ \hline CLIP IE + SelfAtt & 23.54 & 0.851 & 4.928 \\ ISE + SelfAtt & 26.39 & 0.917 & 4.472 \\ CLIP IE + TCEAtt & 27.42 & 0.936 & 4.324 \\ ISE + TCEAtt (TDIRM) & 30.69 & 0.968 & 3.880 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Ablation experiments for different modules of TDIRM. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Embedding** & **PSNR** & **SSIM** & **NIQE** \\ \hline DIE & 25.93 & 0.891 & 4.453 \\ SE + DIE & 28.71 & 0.944 & 4.126 \\ DIE + SegE & 29.72 & 0.953 & 4.045 \\ SE + SegE & 27.64 & 0.925 & 4.237 \\ SE + DIE + SegE (TDIRM) & 30.69 & 0.968 & 3.880 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Ablation experiments for different control embedding of TDIRM. according to the entire chain of vehicle detection. By conducting extensive experiments on both synthetic datasets and a smaller-scale real weather dataset, the impact of these factors on various real-time vehicle detection methods was comprehensively evaluated using multiple metrics. Our findings revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. For instance, blur caused by vehicle motion can greatly weaken vehicle features, leading to a sharp drop in model accuracy. The impact of color changes and noise, while present, is relatively less pronounced. In addition, robustness of vehicle detection is closely linked to its baseline performance and model size. Larger models with more parameters tend to exhibit greater resilience against adverse conditions. However, it is notable that as the severity of corruption intensifies, the performance of models does not decline gradually but experiences a sharp drop. This indicates that there are critical thresholds beyond which the models struggle to maintain their detection capabilities. Meanwhile, results of the class activation map indicate that under adverse conditions, when the data distribution of images changes, the features of the vehicles that the model focuses on are weakened. The activation level of the targets is significantly reduced, making it difficult for them to accurately identify vehicles. So, to address the challenges posed by degraded images in real-world traffic scenarios, TDIRM was proposed based on SDM. TDIRM employs a triple collaborative control mechanism--comprising the image semantics embedding SE for separating degradation information, the degraded image embedding DIE for maintaining structural consistency, and the segmentation embedding SegE for preserving critical targets--significantly enhancing image restoration fidelity. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. TDIRM also achieves superior PSNR, about 30.69 dB, in restored images compared to some dehaze models. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. However, it is essential to acknowledge the limitations of this study. The complex and diverse nature of adverse conditions, coupled with the scarcity of data, prevented us from considering all potential adverse factors comprehensively. This may have led to an incomplete understanding of the full range of challenges that vehicle detection models face in real scenarios. In the future, a more detailed study on the impact of adverse conditions on the robustness of vehicle detection is warranted, which could involve exploring the combined effects of multiple adverse factors.