## 4 Results and Discussion  
This section investigates the object detection result analysis of the CIWHO-ORC technique on different datasets. The proposed model was simulated using the Python tool. The proposed model was tested using the Python tool. The parameter setting is given as follows: learning rate 0.1, dropout: 0.5, epochs: 50, batch size: 7, activation: ReLU. The proposed model was simulated on the Bdd100k dataset. Firstly, Figure 3 visualizes a sample of recognized objects in the frame on the test Bdd100k dataset [23]. The figure portrayed that the CIWHO-ORC technique properly identified the objects as 'persons' present in the test image. Figure 4 shows the confusion matrix of the CIWHO-ORC technique on the Bdd100k dataset, which comprises three classes such as person, vehicle, and two-wheeler. Figure 4 shows that the CIWHO-ORC technique identified 7838 images under the vehicle class proficiently, 5845 images under the person class, and 1324 images under the two-wheeler class. The values that exist in the confusion matrix are transformed into TP, TN, FP, and FN values in Table 1.  
Figure 4: Confusion matrix of CIWHO-ORC technique on Bdd100k Dataset. Figure 3: Sample object detection results on Test Image-Bdd100k Dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Labels** & **True Positive** & **False Negative** & **False Positive** & **True Negative** \\ \hline Vehicle & 7838 & 162 & 168 & 7332 \\ Person & 5845 & 155 & 62 & 9438 \\ Two-wheeler & 1324 & 176 & 263 & 13,737 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Manipulated values of confusion matrix by CIWHO-ORC technique. 0.9574. Similarly, the CIWHO-ORC technique recognized the 'person' with an accuracy of 0.9860, a TNR of 0.9935, an F-score of 0.9818, and an MCC of 0.9705. Moreover, the CIWHO-ORC technique recognized the'vehicles' with an accuracy of 0.9717, a TNR of 0.9812, an F-score of 0.8578, and an MCC of 0.8425. Table 3 provides a brief comparative analysis of the CIWHO-ORC te choice on the test Bdd100k dataset in terms of the average precision (APE) and average recall (ARE). The results show that the CIWHO-ORC technique enhanced the outcomes of the classification of objects. With respect to APE, the CIWHO-ORC technique obtained a higher APE of 0.691, whereas the YOLO-v3, C-Net, and ASPPC-Net techniques attained a lower APE of 0.485, 0.591, and 0.612, respectively. At the same time, with respect to ARE, the CIWHO-ORC technique reached a maximum ARE of 0.890, whereas the YOLO-v3, C-Net, and ASPPC-Net techniques resulted in a minimal APE of 0.817, 0.847, and 0.850, respectively. The comparative APE and ARE analysis of the CIWHO-ORC technique on the recognition of small and large objects is shown in Table 4. The table values exhibit that the CIWHO-ORC technique offered effective classification outcomes with the maximum values of APE and ARE. For instance, with small-object detection, the CIWHO-ORC technique increased the ARE to 0.710, whereas the YOLO-v3, C-Net, and ASPPC techniques reduced the ARE to 0.481, 0.688, and 0.706, respectively. Simultaneously, with large-object detection, the CIWHO-ORC technique improved the ARE to 0.882, whereas the YOLO-v3, C-Net, and ASPPC techniques reduced the ARE to 0.803, 0.845, and 0.848, respectively. Another comparison study of the CIWHO-ORC technique on the identification of tiny objects is shown in Table 5 in terms of the APE and ARE. The results show that the CIWHO-ORC technique outperformed the other methods with the maximum classification results. Based on the APE of tiny object detection, the CIWHO-ORC technique resulted in an increased APE of 0.185, whereas the YOLO-v3, C-Net, and ASPPC techniques decreased the APE to 0.121, 0.153, and 0.154, respectively. Concurrently, based on the ARE for tiny-object detection, the CIWHO-ORC technique led to an improved ARE of 0.864, whereas