## 4 Results and Analysis
### Robustness Results on Synthetic Images  
We show the corruption robustness of YOLOv5 and YOLOv8 models on test data in Tables 4-6 in order to verify the effect of model volume on robustness. Three tables represent the evaluation results under light, middle, and severe conditions, respectively. Evaluation results of the remaining models are supplemented in Section 4.4. A high correlation exists between the robustness of corruption detection and the accuracy of clean detection. Additionally, we plot bar charts for results to provide a visual representation of the robustness metric changes under different corruption levels. Figures 12-14 represent the evaluation results under light, middle, and severe  
Based on the evaluation results, we obtain several findings as below. Adverse weather conditions, blur, and occlusion have the most significant impact on the model's performance. Color and noise have a relatively minor effect on the model's efficacy. Moreover, the performance of the model continually declines with increasing severity of corruption for all types of damage. For instance, severe fog and dark result in poor detection performance by the model. Furthermore, within models of the same category, a larger number of parameters tends to give the model stronger robustness against corruption of all severity. For instance,  
Figure 12: Bar chart of benchmark results of YOLOv5 and YOLOv8. We show change in robustness metric (mAP50) under different corruption types at light level. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & Type (mAP50) (\%) & YOLOv56u & YOLOv5m6u & YOLOv516u & YOLOv8s & YOLOv8m & YOLOv8l & YOLOv8x \\ \hline  & & Clean & 71.6 & 75.8 & 78.4 & 66.2 & 72.0 & 74.9 & 79.3 \\ \hline  & & Rain & 61.1 & 63.3 & 69.9 & 56.5 & 63.2 & 66.4 & 70.6 \\  & & Fog & 60.4 & 64.3 & 69.4 & 54.0 & 61.2 & 63.7 & 69.2 \\  & & Frost & 61.1 & 63.2 & 68.4 & 55.3 & 62.4 & 63.3 & 69.7 \\  & & Snow & 59.2 & 62.1 & 67.0 & 54.4 & 62.1 & 64.4 & 70.0 \\ \hline  & & Brightness & 62.2 & 63.4 & 70.6 & 57.2 & 63.1 & 65.5 & 71.1 \\  & & Dark & 61.7 & 63.6 & 70.7 & 58.3 & 62.1 & 64.0 & 70.1 \\  & & Saturate & 61.0 & 62.7 & 71.6 & 59.8 & 62.9 & 64.7 & 71.8 \\ \hline  & & GaussianNoise & 61.7 & 63.1 & 70.5 & 57.3 & 61.1 & 63.1 & 72.0 \\  & & SpeckleNoise & 60.3 & 62.4 & 70.2 & 56.1 & 60.7 & 64.7 & 71.0 \\  & & ZoomBlur & 31.4 & 32.6 & 34.9 & 27.8 & 29.0 & 30.5 & 35.4 \\  & & MotionBlur & 60.4 & 62.1 & 69.0 & 54.7 & 62.2 & 66.4 & 70.9 \\ \hline  & & ElasticTransform & 60.7 & 61.3 & 68.0 & 55.4 & 62.1 & 65.3 & 69.0 \\  & & JpegCompression & 61.7 & 62.7 & 68.3 & 56.6 & 63.4 & 67.9 & 71.2 \\  & & Occlusion & 10.1 & 12.7 & 16.7 & 7.8 & 9.9 & 14.7 & 18.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The benchmark results of YOLOv5 and YOLOv8. We show robustness metric (mAP50) under each corruption based on the car, truck, and bus classes at middle level. at the same level of corruption, YOLOv8x achieves a higher mAP compared to YOLOv8s, YOLOv8, and YOLOv8l, suggesting that the model size is positively correlated with its ability to resist adverse conditions. Furthermore, to quantitatively analyze the change in model performance with the increasing severity of corruption, Figure 15 illustrates the decline rate of the model's performance compared to clean images.