## 4 Simulation Results
### Analysis on the COCO Dataset  
The COCO dataset is designed to detect and segment various objects that occur in their natural context [32]. As shown in Table 3, the COCO dataset contains various object images, which have been gathered from complex everyday scenes and contains common objects in their natural context. Moreover, objects in this dataset are labeled using per-instance segmentations to aid in precise object localization. Overall, the COCO dataset contains images of 91 object types with a total of two and a half million labeled instances in 328,000 images. Recently, the COCO dataset received extensive attention from researchers investigating various categories of detection including diverse vehicle shapes. Figure 8 shows the vehicle detection results of our proposed method on the COCO dataset. Clearly, Figure 5 depicts the performance of the proposed vehicle detection algorithm on various challenging images of the COCO dataset.  
In most of the instances and under huge illumination variations, almost all of the different vehicles are accurately detected by the proposed methodology. We used other objects, such as motorcycles and persons during this phase. Therefore, those are also accurately located in various images in Figure 8. A few such instances can be seen in the 1 st image of the 2 nd and 3 rd rows, respectively. Similarly, the 6 th image in the bottom row of Figure 8 also depicts the object detection phenomenon. To further validate the superiority of the proposed method, a comparison with fourteen other methods is listed in Table 6 with some important observations.  
* As can be seen for various image resolutions that range from 512 \(\times\) 512 to 800 \(\times\) 800 pixels, the proposed method ranks 1 st among all the compared methods and reports the highest mAP value of 52.31%. The work reported in [42] ranks 2 nd and yields a 50.40% mAP value followed by [41] with a 49.80% mAP value. Our analysis reveals that the work developed in [35; 36] are also an encouraging solution for detecting various objects in the challenging COCO dataset.
* On the COCO dataset, the work reported in [31] yields the lowest (27.89%) mAP value followed by [33], whose method yields a mAP value of 29.10%. Moreover, in the current study, work discussed in [40], which uses the ResNet as a backbone, yields a mAP value of 31.80%, which in the context of current study falls on the lower side.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **G1** & **G2** & **G3** & **G4** & **G5** & **mAP (\%)** \\ \hline
[28] & 100 & 100 & 100 & 98.96 & 99.13 & 99.61 \\ \hline
[29] & 100 & 100 & 100 & 99.73 & 99.21 & 99.78 \\ \hline
[30] & 100 & 100 & 100 & 99.70 & 99.10 & 99.76 \\ \hline
[31] & 100 & 100 & 99.40 & 99.74 & 98.96 & 97.74 \\ \hline
[33] & 99.00 & 99.00 & 98.70 & 98.00 & 98.90 & 98.72 \\ \hline
[34] & 100 & 100 & 100 & 99.00 & 96.50 & 99.10 \\ \hline
[35] & 100 & 100 & 99.00 & 99.64 & 99.06 & 98.34 \\ \hline
[36] & 100 & 100 & 99.40 & 99.74 & 98.96 & 97.74 \\ \hline
[37] & 100 & 98.50 & 100 & 99.50 & 98.10 & 99.22 \\ \hline
[38] & 100 & 100 & 100 & 99.00 & 98.00 & 99.40 \\ \hline
[39] & 99.00 & 100 & 100 & 99.00 & 98.50 & 99.30 \\ \hline
[40] & 100 & 100 & 100 & 99.80 & 99.50 & 99.86 \\ \hline
[41] & 99 & 99 & 98.50 & 98.00 & 99.00 & 98.70 \\ \hline
[42] & 98.90 & 98.50 & 98.00 & 97.50 & 96.10 & 97.80 \\ \hline
**Proposed** & **99.94** & **100** & **100** & **99.73** & **99.96** & **99.92** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vehicle detection comparison (%) on PKU dataset.  
* The crux of this dataset is that the proposed method effectively and reliably detects miscellaneous objects that include vehicles of varying shapes, including motorbikes and jeeps. Furthermore, the proposed method also effectively handles big buses. A few such samples are also in the 1 st and 3 rd columns of Figure 8.  
Figure 8: Vehicle detection results on the COCO dataset.