## 4 Results and Analysis  
### Vehicle Detection Results  
We present visual detection results for eight types of corrosion, including fog, rain, Gaussian noise, zoom blur, brightness, dark, occlusion, and saturate. As illustrated, as the severity of corrosion increases, the number of targets detected by the model decreases, and models with a larger number of parameters exhibit better detection performance. Here are visualizations for robustness evaluation; only the results of YOLOv5 and YOLOv8 are presented, and evaluation results of the remaining models are supplemented in Section 4.4.  
In fog corruption, some detailed information of an otherwise clear image is lost because it is absorbed and scattered by suspended particles in the atmosphere, and the original image is blurred due to atmospheric interference causing contrast reduction, color distortion, and other problems, which greatly affect vehicle detection in the actual situation. Rain comes usually with fog. A large amount of rain streaks not only block the vehicles in the traffic scene but also lead to the emergence of fog and weaken the visual enhancement function of the vehicle camera. The residual rain marks severely affect the image quality of the vehicle camera, causing the image to be blurred and distorted and causing a huge impact on the accuracy of vehicle detection. In addition, the road condition on a rainy day is complex, and there is dense overlap and different scales of the vehicles to be detected in the image, which further exacerbates the difficulty of detection.  
As can be seen from Figure 8, in severe rain or fog, the models are more likely to lose detection vehicles. Moreover, fog has a greater impact on detection performance than rain, and it might be because fog is more likely to cause the loss of important contour information of the target.  
Figure 8: Visualization results under fog and rain (three corruption levels).  
As can be seen from Figure 9, Gaussian noise has a relatively minor impact on the detection results to some extent, but zoom blur significantly affects the detection outcome, even rendering the model almost incapacitated. It can alter the shape of the target to a certain degree and blur its features, posing a great challenge for the model to correctly detect the original target.  
As can be seen from Figure 10, brightness and dark both affect the detection results to some extent. When the degree is mild, the impact is limited, but when it is severe, the model's performance declines significantly. Excessive lighting can weaken the contours and features of the target, while insufficient lighting may result in the camera failing to capture effective target features. They both pose great challenges for the model to correctly detect the original target.  
Figure 10: Visualization results under brightness and dark (three corruption levels).  
Figure 9: Visualization results under Gaussian noise and zoom blur (three corruption levels).  
As can be seen from Figure 11, saturation has a relatively minor impact on the detection results, but occlusion can significantly reduce the model's performance, even rendering the model almost incapacitated. When parts of a vehicle are obscured, it becomes challenging for the model to recognize them due to the loss of their complete features.