## 4 Simulation Results
### Discussion
In contrast, the analysis presented in this study explores the detection ability this method on different datasets on multiple styles of vehicle and on differently color cars. Moreover, this study also investigates the detection ability of this method on varying illuminations and weathers along with different road conditions. * The study in [41] analyzed the DLR Munich vehicle and VEDAI datasets. In their study, mostly high-quality aerial vehicle images are analyzed. Few of these are running on roads, while several parked vehicles are shown. However, our study also reports the use of this method on actual daily life vehicle images from three publicly available datasets. We are optimistic that detailed analysis and comparisons provided in this manuscript will be handy for the research community to modify any algorithm for their specified tasks. * Finally, in [42], the KITTI and the LSVH datasets were explored. Results reported in this study are mostly vans, cars, and bus that run on the main highways. However, our study reports the detection ability of this method on varying illuminations, different weathers, and challenging road conditions from three publicly available datasets. We believe that the analysis provided by our developed method and the detailed comparison listed in this manuscript will provide further insight to the research community. * All of these are useful efforts to solve and automate the vehicle detection problem under varying conditions. For each of the datasets mentioned above, these methods perform well. One of the objectives of the current study was to test and analyze all of the fourteen methods compared in this paper on standard PKU, COCO, and DAWN datasets. The main reason to choose PKU, COCO, and DAWN datasets is that these datasets contain real world and challenging images. For instance, the PKU dataset has five distinct categories that range from normal images to dark night images along with night glare. Similarly, this dataset also contains multiple images that appear in the input along with partial occlusions and different road conditions. Similarly, as mentioned in Section 4, the COCO dataset is also a huge dataset and contains a diverse range of objects. Moreover, the DAWN dataset also contains various real-world situation, such as fog, rain, snow, and the sand. An evaluation of fourteen different methods on these three datasets will be a fair guideline for researchers and beginners to develop, implement, or modify any algorithm for their specified applications. * Out of the datasets that are investigated in this study, we find the DAWN dataset a bit more challenging than the others. The main reason is the inclusion of images in challenging conditions, such as fog, rain, contaminated with sand, or snow. Our study indicates that the sandy images reduce the scene visibility and ultimately reduce the detection accuracy of a detector. The 1 st image in the top row in Figure 11 depicts such conditions in which very low vehicle detection is achieved. Similarly, as shown in the 2 nd image of the top row of Figure 11, low vehicle detection is observed during a rainy night when the head lights of the vehicle are also turned on. In this case an electricity pole also appears, which results in partial occlusion that ultimately results in reduced object detection. * We observe that our proposed method still needs to perform well in different situations, such as when the scene is contaminated with the snow storm or blizzard as shown in the 2 nd row of Figure 11. In such cases, background noise dominates results in low visibility. In this scenario, a Retinex-based image enhancement scheme might be useful. For such a scenario, we suggest that an image dehazing-based enhancement could also be effective. We are optimistic that this proposed solution will essentially enhance the object and image scene, which will later make life easier for any of the vehicle detectors deployed. Ultimately, the application of image enhancement technique will significantly increase the detection ability of object detector.