## 4 Results and Discussion
### Performance of YOLO Models on RTTS Dataset
Some of the images entered into the system for testing purposes from the DAWN dataset trained with GWO-optimized hyperparameters are shown in Figure 21. Compared to Figure 20, the truck in the first row and fourth column is predicted and detected at a higher rate, and a vehicle that is not in the first row and fourth column is not labeled as a vehicle. In the second row and first column, the oncoming vehicle is labeled. The next set of test data images from the DAWN dataset trained with ARO-optimized hyperparameters are shown in Figure 22. Compared to Figure 20, all cars are detected and labeled in the sandstorm image in the first row and third column. The oncoming vehicle in the second row and first column and the truck in the fourth row and fourth column are not labeled as in Figure 20. The next test data images from the DAWN dataset trained with CLEO-optimized hyperparameters are shown in Figure 23. Compared to Figure 20, all cars are detected and labeled in the sandstorm image in the first row and third column. In the same image, an absent vehicle is labeled. Looking at the mAP values in Table 2, the smallest mAP value is 68.00% and the largest value is 72.60%, especially for YOLOv5 and YOLOv9. Looking at the labeled objects in the figures, it is observed that object detection operations are performed in accordance with the mAP values. Figure 19: YOLOv5 + GWO results. Figure 21: Test results with GWO optimizer hyperparameters. Figure 22: Test results with ARO optimizer hyperparameters. Table 5 shows the iteration rate, training time, advantages, and disadvantages of the applied methods for all training using the DAWN dataset, especially our study since it includes all AWCs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 23: Test results with CLEO optimizer hyperparameters. Looking at the results in general, models optimized using YOLOv7 + CLEO or YOLOv9 + GWO, which have the highest mAP value, can be used in applications requiring high accuracies. The models used may vary depending on weather conditions. However, by using the YOLOv7 or YOLOv9 models, the best mAP results can be obtained regardless of the weather conditions. When the prediction images are examined in detail, it is clear that the YOLO models optimized separately using GWO, ARO, and CLEO improve the detection of vehicles, especially when using AWC datasets in dusty weather conditions. All three training configurations (GWO, ARO, CLEO, and original) showed success in object detection, regardless of weather conditions, including scenarios beyond dusty weather. These findings highlight the effectiveness of the YOLOv7 and YOLOv9 algorithms in detecting objects in AWCs and perform even better when combined with the GWO, ARO, or CLEO optimization algorithms. The results and original values of the first 12 hyperparameters of the YOLO versions 5, 7, and 9 models optimized using meta-heuristic optimization algorithms are shown in Tables 6 and 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Ir0** & **Irf** & **Momentum** & **Weight\_Decay** & **Warmup\_Epochs** \\ \hline YOLOv5 & 0.0100 & 0.0100 & 0.9370 & 0.0005 & 3.0000 \\ YOLOv5 + GWO & 0.0124 & 0.0129 & 0.9781 & 0.0008 & 3.2113 \\ YOLOv5 + ARO & 0.0100 & 0.0100 & 0.937 & 0.0005 & 3.0000 \\ YOLOv5 + CLEO & 0.0115 & 0.0109 & 0.9556 & 0.0007 & 3.2030 \\ YOLOv7 & 0.0100 & 0.1000 & 0.937 & 0.0005 & 3.0000 \\ YOLOv7 + GWO & 0.0123 & 0.1286 & 0.9781 & 0.0008 & 3.2113 \\ YOLOv7 + ARO & 0.0120 & 0.1176 & 0.9406 & 0.0006 & 3.0755 \\ YOLOv7 + CLEO & 0.0114 & 0.0125 & 0.9638 & 0.0006 & 3.0110 \\ YOLOv9 & 0.0100 & 0.0100 & 0.9370 & 0.0005 & 3.0000 \\ YOLOv9 + GWO & 0.0106 & 0.0107 & 0.9472 & 0.0006 & 3.0526 \\ YOLOv9 + ARO & 0.0118 & 0.0129 & 0.0006 & 3.1323 & 0.8188 \\ YOLOv9 + CLEO & 0.0116 & 0.0129 & 0.9737 & 0.0006 & 3.0202 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimized and original top 6 hyperparameters in YOLOv5, YOLOv7, and YOLOv9.