## 4 Results and Discussion
### Performance of YOLO Models on DAWN Dataset
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Person** & **Bicycle** & **Car** & **Motorcycle** & **Bus** & **Truck** \\ \hline \multirow{3}{*}{YOLOv5} & Precision & 88.70 & 0.00 & 49.69 & 95.74 & 94.73 & 91.80 \\  & Recall & 57.89 & 0.00 & 82.82 & 62.50 & 72.00 & 73.68 \\  & F1 score & 70.05 & 0.00 & 62.11 & 75.62 & 81.81 & 81.74 \\ \hline \multirow{3}{*}{YOLOv5 + GWO} & Precision & 81.42 & 1.00 & 53.94 & 94.82 & 91.83 & 84.21 \\  & Recall & 61.29 & 50.00 & 82.82 & 67.07 & 76.27 & 77.10 \\  & F1 score & 69.93 & 66.66 & 65.33 & 78.56 & 83.32 & 80.49 \\ \hline \multirow{3}{*}{YOLOv5 + ARO} & Precision & 86.76 & 0.00 & 51.87 & 96.96 & 88.88 & 88.23 \\  & Recall & 62.76 & 0.00 & 83.83 & 78.04 & 64.00 & 80.00 \\  & F1 score & 72.83 & 0.00 & 64.08 & 86.47 & 74.41 & 83.91 \\ \hline \multirow{3}{*}{YOLOv5 + CLEO} & Precision & 85.33 & 89.47 & 51.57 & 94.11 & 95.34 & 92.85 \\  & Recall & 67.36 & 34.00 & 82.82 & 70.32 & 69.49 & 75.58 \\  & F1 score & 75.28 & 49.27 & 63.56 & 80.49 & 80.38 & 83.32 \\ \hline \multirow{3}{*}{YOLOv7} & Precision & 90.66 & 1.00 & 51.86 & 97.33 & 96.49 & 87.01 \\  & Recall & 68.68 & 1.00 & 84.69 & 80.22 & 78.57 & 77.01 \\  & F1 score & 78.15 & 1.00 & 64.34 & 87.95 & 86.61 & 81.71 \\ \hline \multirow{3}{*}{YOLOv7 + GWO} & Precision & 87.65 & 99.00 & 54.38 & 95.52 & 92.85 & 88.15 \\  & Recall & 71.71 & 1.00 & 87.87 & 70.33 & 91.23 & 81.70 \\  & F1 score & 78.88 & 99.49 & 67.18 & 81.01 & 92.03 & 84.80 \\ \hline \multirow{3}{*}{YOLOv7 + ARO} & Precision & 88.09 & 99.00 & 55.00 & 95.34 & 92.15 & 87.84 \\  & Recall & 74.74 & 1.00 & 89.79 & 82.00 & 79.66 & 84.42 \\  & F1 score & 80.86 & 99.49 & 68.21 & 88.16 & 85.45 & 86.09 \\ \hline \multirow{3}{*}{YOLOv7 + CLEO} & Precision & 88.75 & 99.00 & 53.12 & 95.89 & 96.34 & 87.83 \\  & Recall & 71.00 & 1.00 & 86.73 & 70.00 & 84.94 & 80.24 \\  & F1 score & 78.88 & 99.49 & 65.88 & 80.92 & 90.28 & 83.86 \\ \hline \multirow{3}{*}{YOLOv9} & Precision & 82.43 & 1.00 & 51.57 & 96.96 & 96.15 & 91.17 \\  & Recall & 64.89 & 79.76 & 83.67 & 70.32 & 78.12 & 72.09 \\ \cline{1-1}  & F1 score & 72.61 & 88.74 & 63.81 & 81.51 & 86.20 & 80.51 \\ \hline \multirow{3}{*}{YOLOv9 + GWO} & Precision & 87.67 & 1.00 & 51.85 & 97.33 & 93.75 & 90.90 \\ \cline{1-1}  & Recall & 67.36 & 79.76 & 84.84 & 73.00 & 76.27 & 80.45 \\ \cline{1-1}  & F1 score & 76.18 & 88.74 & 64.36 & 83.42 & 84.11 & 85.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results using the DAWN dataset (%). Figure 14: Confusion matrix diagram for YOLOv9 with CLEO parameters. Figure 15: YOLOv7 + CLEO results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **Person** & **Bicycle** & **Car** & **Motorcycle** & **Bus** & **Truck** \\ \hline \multirow{3}{*}{YOLOv9 + ARO} & Precision & 91.17 & 1.00 & 49.41 & 98.64 & 96.15 & 95.89 \\  & Recall & 65.26 & 50.00 & 85.85 & 80.21 & 78.12 & 80.45 \\  & F1 score & 76.06 & 66.66 & 62.72 & 88.47 & 86.20 & 87.49 \\ \hline \multirow{3}{*}{YOLOv9 + CLEO} & Precision & 83.95 & 1.00 & 54.24 & 98.46 & 84.37 & 85.33 \\  & Recall & 72.34 & 50.00 & 84.69 & 70.32 & 75.00 & 77.10 \\ \cline{1-1}  & F1 score & 77.71 & 66.66 & 66.12 & 82.04 & 79.40 & 81.00 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._