## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Avg. Precision**} & \multicolumn{2}{c}{**Avg. Recall**} \\ \cline{2-5}  & **Small** & **Large** & **Small** & **Large** \\ \hline YOLO-v3 & 0.481 & 0.631 & 0.803 & 0.912 \\ C-Net Model & 0.688 & 0.675 & 0.845 & 0.852 \\ ASPPC-Net Model & 0.706 & 0.665 & 0.848 & 0.853 \\ CIWHO-ORC & 0.710 & 0.902 & 0.882 & 0.926 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: APE and ARE Analysis of CIWHO-ORC technique on Small and Large Object Classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **TNR** & **F-Score** & **MCC** \\ \hline Vehicle & 0.9787 & 0.9776 & 0.9794 & 0.9574 \\ Person & 0.9860 & 0.9935 & 0.9818 & 0.9705 \\ Two-wheeler & 0.9717 & 0.9812 & 0.8578 & 0.8425 \\ Average & 0.9788 & 0.9841 & 0.9396 & 0.9235 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results of CIWHO-ORC technique on Bdd100k Dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Avg. Precision** & **Avg. Recall** & **Avg. Prec. \(-\)1** & **Avg. Prec. \(-\)2** & **Avg. Prec. \(-\)3** \\ \hline YOLO-v3 & 0.485 & 0.817 & 0.768 & 0.425 & 0.261 \\ C-Net Model & 0.591 & 0.847 & 0.800 & 0.620 & 0.354 \\ ASPPC-Net Model & 0.612 & 0.850 & 0.807 & 0.632 & 0.397 \\ CIWHO-ORC & 0.691 & 0.890 & 0.863 & 0.715 & 0.495 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: APE and ARE Analysis of CIWHO-ORC technique on Bdd100k Dataset. the YOLO-v3, C-Net, and ASPPC techniques decreased the APE to 0.614, 0.802, and 0.816, respectively. The experimental result analysis of the CIWHO-ORC technique was examined under traffic light datasets, which comprised of eight types of different traffic lights. Figure 5 illustrates the confusion matrices produced by the CIWHO-ORC technique for recognizing different traffic lights. The CIWHO-ORC technique identified 45 images under class 1, 46 images under class 2, 49 images under class 3, 46 images under class 4, 49 images under class 5, 48 images under class 6, 47 images under class 7, and 45 images under class 8. Table 6 offers the overall traffic signal classification results obtained by the CIWHO-ORC technique. The results reported that the CIWHO-ORC technique effectively identified distinct classes of traffic signals. For instance, with a green light (1), the CIWHO-ORC technique gave an accuracy of 0.9650, a TNR of 0.9743, an F-score of 0.8654, and an MCC of 0.8461. Meanwhile, with a green right (3), the CIWHO-ORC technique provided an accuracy of 0.9850, a TNR of 0.9857, an F-score of 0.9423, and an MCC of 0.9346. Eventually, with the red light (5), the CIWHO-ORC technique obtained an accuracy of 0.9925, a TNR of 0.9943, an F-score of 0.9703, and an MCC of 0.9661. At last, with a red light (8), the CIWHO-ORC technique gave an accuracy of 0.9700, a TNR of 0.9943, an F-score of 0.8696, and an MCC of 0.8569. Figure 5: Confusion matrix of CIWHO-ORC technique on Traffic light dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Avg. Precision (Tiny)** & **Avg. Recall (Tiny)** \\ \hline YOLO-v3 & 0.121 & 0.614 \\ \hline C-Net Model & 0.153 & 0.802 \\ \hline ASPPC-Net Model & 0.154 & 0.816 \\ \hline CIWHO-ORC & 0.185 & 0.864 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: APE and ARE Analysis of CIWHO-ORC technique on Tiny Object Classification.