## 4 Results and Discussion
### Performance of YOLO Models on RTTS Dataset  
Table 4 shows the training mAP results of the YOLOv5, YOLOv7, and YOLOv9 models using the RTTS dataset, with original and optimized hyperparameters with the GWO, ARO, and CLEO optimization algorithms. Table 4 also shows the results of other studies using the RTTS dataset mentioned in this paper. When the results are analyzed, it is seen that the mAP value of the YOLOv5-Fog model in [14] is higher than all the models of YOLOv5 and YOLOv7. The models with the best mAP values are the YOLOv9 models. The objects in the datasets and the number of labels of the objects are shown in Figure 3. Here, it is seen that the person tag is labeled considerably more than the other objects except for cars in Figure 3. Table 4 shows that when trained with the original hyperparameters of YOLOv5, YOLOv7, and YOLOv9 using the RTTS dataset, the mAP results are 75.60%, 76.80%, and 78.40%, respectively. It is observed that the optimization algorithms used with YOLOv5 do not affect the mAP result in YOLOv5 in any way. The YOLOv7 + GWO model realized a performance improvement of 0.13%, the YOLOv7 + ARO model realized a performance improvement of 0.65%, and the YOLOv7 + CLEO model realized a performance improvement of 1.04% compared to the original YOLOv7 model. The YOLOv9 + GWO model realizes a performance increase of 0.51%, the YOLOv9 + ARO model realizes a performance increase of 1.02%, and the YOLOv9 + CLEO model realizes a performance increase of 1.14% compared to the original YOLOv9 models. \begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **RTTS (mAP)** \\ \hline IA-YOLO [8] & 52.00 \\ TogetherNet [1] & 61.55 \\ Faster-RCNN [15] & 51.30 \\ RetinaNet [19] & 53.10 \\ YOLOv5-Fog [14] & 77.80 \\ YOLOv5 & 75.60 \\ YOLOv5 + GWO (Ours) & 75.60 \\ YOLOv5 + ARO (Ours) & 75.60 \\ YOLOv5 + CLEO (Ours) & 75.59 \\ YOLOv7 & 76.80 \\ YOLOv7 + GWO (Ours) & 76.90 \\ YOLOv7 + ARO (Ours) & 77.30 \\ YOLOv7 + CLEO (Ours) & 77.60 \\ YOLOv9 & 78.40 \\ YOLOv9 + GWO (Ours) & 78.80 \\ YOLOv9 + ARO (Ours) & 79.20 \\ YOLOv9 + CLEO (Ours) & 79.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mAP (%) comparison on the RTTS dataset. Figure 16: YOLOv5 + CLEO results. Compared to the original YOLOv7 model, the YOLOv9 + GWO model realizes a performance increase of 0.51%, the YOLOv9 + ARO model realizes a performance increase of 1.02%, and the YOLOv9 + CLEO model realizes a performance increase of 1.14% compared to the original YOLOv9 model. Also, Figure 17 shows the graphical results of all training using the RTTS dataset after 50 epochs. An example of graphical data showing all the changes that occurred throughout the training is shown in Figures 18 and 19. In our study, the increase in object detection performance with hyperparameter optimization shows that the intended goal was achieved, especially when including AWCs and only traffic roads. The differences are that we only trained on AWCs and used GWO, ARO, and CLEO for hyperparameter optimization. In [11], the 12 hyperparameters of YOLOv5 are used for object detection using the MWOA optimization algorithm. Since the used KITTI [45] dataset usually contains normal, clean images, it is not known how it will perform in AWCs. Therefore, it uses the dataset with normal weather conditions, and so it does not address AWCs. Additionally, it uses only the older version of YOLOv5. After training the DAWN dataset with the default parameters, Figure 20 shows some images presented to the system for testing. In the first row and third column, some vehicles are not detected. In the first column of the second row, the oncoming vehicle is not detected. In other examples, the first row detects objects that are not in the fourth column.