## 5 Conclusions  
This study provides significant insights into improving the performance of object detection models under rainy conditions. Firstly, we explored for the first time the training of rainy weather object detection models using pre-trained RTDETR models, comparing them with YOLOv8. The approach we used to generate training data ensured a diverse set of rainy images, crucial for achieving accurate models. Secondly, we evaluated the models' performance using three different methods for generating rainy images and found that no single method could be universally applied to all pre-trained object detection models, posing a challenging question for future research. Furthermore, we recommend integrating RTDETR with the neural style transfer or analytical methods to enhance the performance of rainy weather \begin{table}
\begin{tabular}{l c c c c} \hline \hline Model & Precision (P) & Recall (R) & mAP50 & mAP50-95 \\ \hline \multicolumn{5}{c}{\%} \\ \multicolumn{5}{c}{Improvement = (\(\frac{\text{x}_{i}\cdot\text{x}}{\text{x}}\times 100\)), where x\({}_{i}\) represents the object detection model, and x represents the baseline.} \\ \hline RTDETRx\_non & \multicolumn{5}{c}{As the baseline for RTDETRx-based model} \\ YOLOv8x\_non & \multicolumn{5}{c}{As the baseline for YOLOv8x-based model} \\ RTDETRx\_A & 1.77 & \(-\)0.20 & 4.60 & 13.89 \\ RTDETRx\_CG & \(-\)1.21 & \(-\)14.12 & \(-\)8.11 & \(-\)3.74 \\ RTDETRx\_NST & \(-\)3.70 & **16.35** & **6.26** & **15.50** \\ YOLOv8x\_A & **3.08** & \(-\)15.73 & \(-\)1.26 & \(-\)2.46 \\ YOLOv8x\_CG & 3.07 & \(-\)8.58 & 1.73 & 0.92 \\ YOLOv8x\_NST & \(-\)0.07 & \(-\)9.10 & \(-\)2.02 & \(-\)2.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Improvement in performance for different object detection models. object detection models, providing valuable insights for practical applications. Specifically, combining with neural style transfer yields significant improvements of 16.35% in Recall (R) and 15.50% in mAP50-95, demonstrating the robustness of this approach against rainy conditions and highlighting the excellence of the proposed method in this study. Using image generation techniques to construct training data under rainy weather conditions is an efficient method of data collection. While obtaining real rainy images through long-term observation is possible, it still has its limitations. It is understandable that collecting images of various objects in rainy conditions is more challenging than in clean environments. However, advancements in image generation technology bring hope. Similar to the approach in this study, we can first augment clean images to generate diverse images and then apply rainy weather effects to all clean and augmented images using image generation methods. These effects can be adjusted through parameters to simulate various rain intensities, from light drizzles to heavy storms. Such training data strengthen the robustness and reliability of object detection models in terms of accuracy. Therefore, we believe this is a research area worthy of further exploration. It is worth noting that the results of this study are valuable both in academic research and practical applications. In academia, we expanded the understanding of rainy weather object detection models and proposed new training methods and performance evaluation strategies. In practical applications, our research findings can be applied to various scenarios requiring object detection in rainy environments, such as self-driving cars and smart city surveillance systems. These application areas stand to benefit from the effective methods and technologies proposed in this study, enhancing system performance and reliability. Overall, the results of this study make important contributions to the modeling of rainy weather object detection models, offering valuable insights and guidance for future research and applications. Conceptualization, C.-Y.H., Y.-C.C. and T.-T.C. ; methodology, C.-Y.H., Y.-C.C. and T.-T.C. ; software, Y.-C.C., T.-T.C. and T.-H.L. ; validation, C.-Y.H., Y.-C.C., T.-T.C., T.-Y.C.