## 1 Introduction
We then conducted detailed experiments on the aforementioned synthetic datasets, testing the mAP and false alarm rate of different models for vehicle detection under various adverse conditions and calculating the corresponding mAP degradation rate. Additionally, to verify the reliability of the synthetic datasets, we performed the same experiments on a small-scale real-world adverse weather dataset. Finally, we conducted interpretative analysis using class activation mapping to observe the changes in the focus of detection models under different adverse conditions. Finally, we draw relevant conclusions to provide directions for the robustness optimization of vehicle detection models. Based on the evaluation results, we find:  
1. In the real world, factors that have the greatest impact and are most likely to occur on vehicle detection in reality include adverse weather conditions, color variations due to lighting conditions, noise caused by sensor operations, blur resulting from motion, transform during image transmission and compression, occlusions by other objects, etc. Thereinto, adverse weather, blur, and occlusion have the greatest impact on vehicle detection, while the effects of color changes and noise are relatively minor. 2. The robustness of vehicle detection is positively correlated with its baseline performance, and the size of the model is also positively related to its ability to withstand adverse conditions. In other words, a larger number of parameters endows the model with greater robustness. Meanwhile, as the severity of corruption increases, the performance of the model does not decline steadily but rather drops sharply. 3. The blur of vehicle features under adverse conditions leads to changes in the data distribution of images, and the features of vehicles that models focus on are weakened, making it challenging for models to detect vehicles. And to a certain extent, synthetic data can represent image corruption in real-world scenarios to use for robustness evaluation of vehicle detection. Based on these findings, a traffic degradation image restoration model (TDIRM) based on stable diffusion is proposed to effectively restore degraded images in real traffic scenarios, thereby enhancing vehicle detection robustness in adverse conditions. We introduce an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Comparative experiments show that through triple conditional control, the restoration by TDIRM achieves high fidelity. And vehicle detection improves mAP by 6 on real-world dense fog images while significantly boosting precision and recall. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. In summary, our contributions mainly contain two parts:  
1. By robustness benchmark evaluation, we revealed the extent and patterns of how various adverse conditions limit vehicle detection performance, providing guidance and direction for optimizing detection robustness. 2. We proposed TDIRM to efficiently restore degraded images with high fidelity and consistency in real traffic scenarios and thereby enhance the robustness of vehicle detection. Figure 1: There are 14 types of corruption that can affect 2D vehicle detection, categorized into four categories: weather, color, noise, and blur, transform.