## 4 Simulation Results
### Analysis on the PKU Dataset  
The PKU dataset is a collection of diverse vehicle images that are captured under diverse conditions [27]. As shown in Table 4, that this dataset contains a total of 3977 diverse vehicle images. The developers of the PKU dataset divided the vehicles into five distinct and different categories, which they refer as G1, G2, G3, G4, and G5. Out of 3977 vehicle images, the PKU dataset also contains a total of 4263 visible license plateswhose pixel resolution varies from 20-62 pixels, which are captured therein. Figure 7 shows a few of the vehicle detection results of our proposed YOLO-based method on all of the five categories of the PKU dataset. As can be seen in the first three rows of Figure 7, for the G1-G3 categories, the proposed method locates all the vehicles in the input images. For the G4 category as shown by the fourth row in the Figure 7, it is obvious that the proposed method is able to locate vehicles that just expose their front bonnet. Moreover, the PKU-G4 category also contains extreme reflective glare. It is always very challenging for any detection algorithm to perform accurately under such circumstances. However, as can be seen, the proposed method handles the aforesaid scenario effectively. Moreover, the proposed method also performs well on the G5 category, which contains multiple vehicles per image. It can be seen that for different view angles along with the partially occluded vehicles, the proposed method performs well and in most of the instances detects all such vehicles. Figure 7 also reveals that the PKU-G3, G4, and G5 pose a challenge to any detection algorithm due to the fact that the illuminations change abruptly. A few of the images shown in the 3 rd, 4 th, and 5 th rows in Figure 7 have a background that is dark black, or in which the head lights of the vehicles are turned on. In such cases, the proposed method performs well and up to task by locating all the vehicles that appear therein. Table 5 lists the comparison of the proposed method on the PKU dataset with fourteen other methods. Since we collected most of the data from Pakistani cities, for a fair comparison we tested the methods reported in [28; 29; 30; 31] and [33; 34; 35; 36; 37; 38; 39; 40; 41; 42] on the whole PKU dataset along with our developed method. Table 5 lists the detailed results with important observations. * From Table 5, it is evident that all the compared methods except [33; 35; 36; 37; 41] and [42] yield 100% detection accuracy on the PKU-G1-G3 categories. * On the G4 category, the proposed method ranks 3 rd among all the compared fourteen methods in terms of detection accuracy. On the other hand, on the G5 category, our developed method outperforms all the compared methods. The PKU G5 is a challenging category due to fact it contains multiple vehicles per image and also contains several disguising crosswalks that pose a threat to any vehicle detection algorithm. * From Table 5, we also observe that for G1, G2, and G3 categories, the methods developed in [28; 29; 30; 38; 40] produce 100% vehicle detection result. Our proposed method yields 99.94% vehicle detection accuracy on the G1 category and 100% for the G2 and G3 categories. Therefore, we observe that methods shown in Table 5 have solved the challenge of vehicle detection on these three categories as most of them yield at least 99% accurate vehicle detection.