## 4 Simulation Results
### Discussion  
Although the analysis presented above familiarizes the readers with the feasibility of our developed method to detect diverse vehicles under diverse range of environments, the discussion below will give further insight to the readers. * Methods compared in this study are state-of-the-art object detectors. We observed that specific method performs well on a specific dataset but are challenged by other datasets. For instance, the work developed in [28] investigates BIT-Vehicle and UA-DETRAC datasets only. These datasets mostly contain high quality frontal view of the different vehicles with image resolution of 1920 \(\times\) 1080 to 1600 \(\times\) 1200 pixels. In contrast, the method proposed in current study explores three different datasets that have variations, such as different road conditions, varying weathers, or complex backgrounds. Moreover, the study presented in this manuscript also explores the detection ability of this method on three standard and publicly available datasets. * The works discussed in [29; 34] mostly focus on KITTI and the DAWN datasets that contain the variations as described earlier. However, we also explore their detection ability on five different classes of the PKU dataset that contain huge road and traffic variations along with our proposed method. This will essentially provide a nice baseline to beginners and researchers to develop their specified tasks. * The work reported in [30] investigates the generic PKU dataset in its five distinct categories. However, this study further explores the detection capability of [30] on the COCO and the DAWN datasets. Moreover, the detection accuracy of the method proposed in this study provides a fair insight into vehicle detection in various scenarios. * The method developed in [31] examined the CARLA dataset, which we believe is a limited and relatively small vehicle dataset. The findings presented in this study extend the detection capability of this method to three other datasets. In addition, its detection comparison with the proposed method and several other techniques provides much detailed insight about issues in the vehicle detection domain. * In [33; 37], the PASCAL VOC 2007 dataset is explored only. Moreover, work in [33] also analyzes the subdomain of the COCO dataset to show the detection of trains only. In contrast, this study explores the detection capability of [33] on various vehicle classes of the COCO dataset along with the PKU and DAWN datasets. Moreover, the detailed comparison provided in the earlier sections provides a fair baseline to the research community. Furthermore, the work in [37] explored the PASCAL dataset  
Figure 10: Computational complexity comparison with fourteen other approaches [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. that already contains annotated images of various objects. This study further expands the detection capability of this method to three different vehicle datasets. Finally, the detailed analysis and comparison provided in earlier section hints towards additional modifications of this algorithm. * The works in [35; 36; 39; 40] were validated on the MS COCO dataset to detect various objects. The experiments reported in this study extend the detection analysis of the aforementioned approaches to PKU and DAWN datasets as well. Since our method also explores the vehicle detection on these datasets, it will be convenient for researchers and practitioners to choose the appropriate algorithm for their specified applications. Moreover, the work listed in [40] reports the detection of various objects, such as pedestrians, statues, or animals. However, this study reports the detection ability of this algorithm on actual and real-world vehicle images along with several other approaches. * In [38], the PETS2009 and the changedetection.net 2012 datasets are explored. Results analyzed in their study are mostly standard high quality frontal view images of monocolor cars running on a main highway.