## 4 Results and Discussion  
This section evaluates the detection capabilities of the eight trained object detection models in this study, focusing on several aspects: whether the proposed method is effective, which method of generating rainy images enhances model recognition ability, the adaptability of RTDETR and YOLOv8 to the generated rainy image data, and the improvement in detection capabilities after adding rainy images as training data. The DAWN dataset mainly consists of vehicles such as cars, motorcycles, and pedestrians on the road. Therefore, these three categories are evaluated for detection accuracy. Considering that the number of instances varies among classes in rainy images, a weighted average is used to evaluate the overall accuracy of each metric. In other words, classes with more instances have higher weights, while classes with fewer instances have lower weights. We believe that this approach provides a fair and objective representation of model performance compared to simple averages. Table 2 presents the testing results of the eight trained object detection models in this study. RTDETRx_non and YOLOv8x_non represent the object detection models using only original and augmentation images. The bold detection accuracy metric values indicate the best detection accuracy achieved by the corresponding model in that metric. The results show that RTDETRx_NST has the best performance in this study, followed by RTDETRx_A and then YOLOv8x_CG. These three models, when compared with their corresponding RTDETRx_non and YOLOv8x_non, demonstrate a significant improvement in object detection accuracy when rainy images are included in the training data. However, it is worth noting that this effect is not observed in the RTDETRx_CG, YOLOv8x_A, and YOLOv8x_NST models. Among the pre-trained models, YOLOv8x_CG exhibits the best object detection capability compared to other pre-trained YOLOv8x models. However, the rainy images generated using CycleGAN do not yield outstanding performance when applied to the pre-trained RTDETRx model. Regarding the performance of pre-trained models, it is evident that RTDETRx_non and YOLOv8x_non have respective advantages in different metrics when rainy images are not included. RTDETRx_non demonstrates better category prediction ability, while YOLOv8x_non shows better detection sensitivity. However, when rainy images are added to the training data, significant differences emerge. Undoubtedly, the performance of pre-trained RTDETRx models stands out, especially the RTDETRx_NST and RTDETRx_A models, which both exhibit a significant improvement in object detection accuracy under rainy conditions. Compared to YOLOv8x, RTDETRx demonstrates advantages in real-time performance and hardware deployment requirements. The higher FPS of RTDETRx indicates its ability to process images and generate detection results more quickly, which is crucial for real-time object detection applications. Additionally, RTDETRx has lower GFLOPs and parameter counts, suggesting greater efficiency in computation and storage. Therefore, deploying RTDETRx models in resource-constrained environments is easier and more efficient compared to YOLOv8x. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Precision (P)** & **Recall (R)** & **mAP50** & **mAP50-95** & **FPS** & **GFLOPs** & **Parameters** \\ \hline RTDETRx\_non & 0.9285 & 0.5976 & 0.7891 & 0.4651 & 36.90 & 222.5 & 65,483,876 \\ YOLOv8x\_non & 0.8708 & 0.6585 & 0.7634 & 0.4883 & 34.25 & 257.4 & 68,131,272 \\ RTDETRx\_A & **0.9449** & 0.5964 & 0.8254 & 0.5297 & 37.04 & 222.5 & 65,483,876 \\ RTDETRx\_CG & 0.9173 & 0.5132 & 0.7251 & 0.4477 & 37.04 & 222.5 & 65,483,876 \\ RTDETRx\_NST & 0.8941 & **0.6953** & **0.8385** & **0.5372** & 37.17 & 222.5 & 65,483,876 \\ YOLOv8x\_A & 0.8976 & 0.5549 & 0.7538 & 0.4763 & 34.56 & 257.4 & 68,131,272 \\ YOLOv8x\_CG & 0.8975 & 0.6020 & 0.7766 & 0.4928 & 34.56 & 257.4 & 68,131,272 \\ YOLOv8x\_NST & 0.8702 & 0.5986 & 0.7480 & 0.4768 & 34.25 & 257.4 & 68,131,272 \\ \hline \hline \end{tabular}
\end{table}