## 5 Results and Discussion
### Performance of R-CNN  
According to the evaluation results shown in Table 6, R-CNN-based models' performance in different weather conditions shows a clear correlation between the complexity of the weather conditions and the decrease in precision, recall, and mAP scores. In clear weather, all models performed relatively well, with precision and recall scores ranging from 0.475 to 0.711, mAP@.5 scores ranging from 0.672 to 0.746, and mAP@ [0.5:0.95] scores ranging from 0.269 to 0.338. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Data Subset** & **Precision** & **Recall** & **mAP@.5** & \begin{tabular}{c} **mAP@** \\ **[0.5:0.95]** \\ \end{tabular}  \\ \hline \multirow{6}{*}{R-CNN} & Clear & 64.2\% & 45.5\% & 67.2\% & 26.9\% \\ \cline{2-6}  & Rain & 49.4\% & 42.4\% & 41.1\% & 12.7\% \\ \cline{2-6}  & Sunset & 51.2\% & 45.9\% & 46.4\% & 15  
The Faster R-CNN, Cascade R-CNN, and Sparse R-CNN models are leading in terms of mAP success rates, with the Faster R-CNN and Sparse R-CNN having the highest precision score of 0.692 and the Cascade R-CNN having the highest recall score of 0.552. They also showed the largest performance improvement compared to the standard R-CNN model. Faster R-CNN improved by 5% in precision and 4.8% in recall, Cascade R-CNN improved by 6.9% in precision and 9.7% in recall, and Sparse R-CNN improved by 5% in precision and 8.7% in recall. Under more challenging conditions, such as rain and changing light, due to the late hours of the day, R-CNN models saw a significant drop in performance. With the improvement in models and their detection accuracy under clear conditions, the gap in the performance between clear and worsened conditions may also increase. However, there are some exceptions, especially with the newer architectures, such as Cascade and Sparse R-CNN. Each model responds differently to adverse road conditions, but one of the common phenomena was a significant decrease in the accuracy of detection under night conditions. When compared to models' performance under clear conditions, there was a decrease recorded, on average, by 26%, 23.5%, 20.3%, and 41.2% in mAP@5 for the rain, sunset, evening, and night subsets, respectively. The continuous change in the mAP accuracy throughout the different versions of R-CNN models is marked in Table 6, while the accuracy of the basic R-CNN model is considered as the baseline starting point. The difference in accuracy is calculated considering the current highest value achieved for individual data subsets. A comparison of the overall performance of the models, when the mean value of accuracy across all conditions is calculated, is shown in Table 7. We can see that the various object detection models function differently. To better understand the strengths and weaknesses of each model, we will compare them in several ways, including model size, number of parameters, inference time, and performance. * Model size and inference time: The model size is an important factor to consider when deploying the model in real-world scenarios, as larger models require more memory and computational resources. The inference time, on the other hand, is important for real-time applications, where quick responses are essential. With the smallest model size of 149 MB, the basic R-CNN model provides the inference of 189.8 ms and the least accurate detection. R-CNN successors, Fast and Faster R-CNN, come with an increase in model size as well as in detection accuracy and an improvement in inference speed. Mask and Sparse R-CNN, whose size is more than double that of R-CNN, can achieve both short inference time and higher detection performance. While a multi-stage object detector, Cascade R-CNN, can provide more accurate results, the processing time of the inference is still high. * Parameters: The number of parameters is another critical factor in determining the model's performance. A model with more parameters has the potential to learn more complex relationships in the data, which can lead to better performance.