## 4 Simulation Results
### Analysis on the DAWN Dataset  
The DAWN dataset is designed to investigate the performance of recent vehicle detection methods on a broad range of natural images including adverse weather conditions. The DAWN dataset contains 1000 image of significant variation in terms of vehicle size and category along with pose variation, non-uniform illumination, position, and occlusion from real traffic environments. Additionally, it exhibits a systematic variation for traffic scenes, for instance, bad winter weather, heavy snow, sheet rain, sand, and dust storms. Figure 9 shows detailed results on fog, sand, rain, and snow situations with important observations. * For the snow category as seen in top row of Figure 9, it is obvious that many times the vehicles are partially visible due to adverse weather conditions, such as fog that is normally experienced in severe winters in areas of various parts of the world. However, our developed method handles all such situations except the 2 nd last image of front row in Figure 9, where it is obvious that the vehicle is not visible to the human eye as well. * For a considerably rainy day as seen in second row of Figure 9, the proposed method accurately locates multiple vehicles that appear therein. In this case, the image scene variations, such as shown in the 2 nd and 4 th images of the second row in Figure 9 indicates that the proposed method is unaffected by such changes in the image scene. Similarly, the skyscrapers in the vehicle's background as shown in the 5 th image of the 2 nd row in Figure 9 also do not affect the detection ability of our developed method. * For a sand situation as indicated in the third row of Figure 9, the proposed method detects all vehicles that appear there in such challenging conditions. In such situations, visibility is normally very low, which poses threats to most of the machine learning algorithms. Particularly, the first two images in the 3 rd row of Figure 9 have intra-class scene variations, i.e., both are images effected by sand storms and yet appear differently to the human eye. Even in such cases, our developed method performs well and detects most of the instances that appear in such condition. The 3 rd image in this row is quite challenging for human observers as well. However, as indicated there, our developed method handles such situations by successfully locating the vehicles that appear in such scene images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Backbone** & **Data** & **Input Size** & **Multi Scale** & **mAP(\%)** \\ \hline
[28] & CSPDarkNet53 & trainval35K & \(512\times 512\) & False & 47.62 \\ \hline
[29] & CNN & trainval35K & \(512\times 512\) & False & 48.00 \\ \hline
[30] & R-CNN & trainval35K & \(512\times 512\) & False & 46.20 \\ \hline
[31] & BottleneckCSP & trainval35K & \(512\times 512\) & False & 27.89 \\ \hline
[33] & VGGNet-16 & trainval35K & \(512\times 512\) & False & 29.10 \\ \hline
[34] & ResNet-101-FPN & trainval35K & \(512\times 512\) & False & 38.30 \\ \hline
[35] & VGGNet-16 & trainval35K & \(800\times 800\) & False & 41.00 \\ \hline
[36] & ResNet-101 & trainval35K & \(800\times 800\) & False & 48.40 \\ \hline
[37] & ResNet-101 & trainval35K & \(512\times 512\) & False & 39.30 \\ \hline
[38] & CNN + SVM & trainval35K & \(512\times 512\) & False & 49.05 \\ \hline
[39] & BN + ReLU & trainval35K & \(512\times 512\) & False & 32.98 \\ \hline
[40] & ResNet-C4-FPN & trainval35K & \(512\times 512\) & False & 31.80 \\ \hline
[41] & ResNet-50 & trainval35K & \(512\times 512\) & False & 49.80 \\ \hline
[42] & SiNet & trainval35K & \(512\times 512\) & False & 50.40 \\ \hline
**Proposed** & **CSP** & **trainval35K** & **512\(\times\) 512** & **False** & **52.31** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Vehicle detection accuracy comparison on the COCO dataset. * The bottom row in Figure 9 is a case when the scene is dominated by snow.