# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the PKU Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Vehicle Conditions**} & **Input Image Resolution (Pixels)** & \multirow{2}{*}{**No. of Images**} & \multirow{2}{*}{**No. of Plates**} & \multirow{2}{*}{**Plate Height (Pixels)**} \\ \hline G1 & Cars on roads; ordinary environment at different daytimes; contains only one license plate per image & \multirow{2}{*}{\(1082\times 728\)} & \multirow{2}{*}{\(810\)} & \multirow{2}{*}{\(810\)} & \multirow{2}{*}{\(35\)–\(57\)} \\ \hline G2 & Cars/trucks on main roads at different daytimes with sunshine; only one license plate in each image & \multirow{2}{*}{\(1082\times 728\)} & \multirow{2}{*}{\(700\)} & \multirow{2}{*}{\(700\)} & \multirow{2}{*}{\(30\)–\(62\)} \\ \hline G3 & Cars/trucks on highways during the night; one license plate per image & \multirow{2}{*}{\(1082\times 728\)} & \multirow{2}{*}{\(743\)} & \multirow{2}{*}{\(743\)} & \multirow{2}{*}{\(29\)–\(53\)} \\ \hline G4 & Cars/trucks on main roads; daytimes with reflective glare; one license plate in input images & \multirow{2}{*}{\(1600\times 1236\)} & \multirow{2}{*}{\(572\)} & \multirow{2}{*}{\(572\)} & \multirow{2}{*}{\(30\)–\(58\)} \\ \hline G5 & Cars/trucks at roads junctions with crosswalks with several plates per image & \multirow{2}{*}{\(1600\times 1200\)} & \multirow{2}{*}{\(1152\)} & \multirow{2}{*}{\(1438\)} & \multirow{2}{*}{\(20\)–\(60\)} \\ \hline  & **Complete PKU dataset** & \multirow{2}{*}{\(3977\)} & \multirow{2}{*}{\(4263\)} & \multirow{2}{*}{\(20\)–\(62\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The PKU dataset description. **Vehicle+LP detection: G4-category:** The 4 th row in Figure 3 demonstrates a few images from the G4 category. This category also contains one license plate in an image, but those are captured in a difficult situation of reflective glare that affects the image quality and the LP area appearance. However, in this case, our applied object detectors handle them efficiently. For each of the different images shown in the fourth row of Figure 3, the good performance of the applied detectors to localize both vehicles and the LP of that vehicle is evident. **Vehicle+LP detection: G5-category:** The last row in Figure 3 demonstrates a few images from the G5 category. This category contains a few LPs in an image. As shown in the last row of Figure 3, all the instances of object detection are completely achieved. In particular, the first image in the fifth row shows the object from an angle, which is also correctly spotted by the applied detectors. The rest of the images in this row contain at least two vehicles along with two LPs that are accurately detected. Table 2 lists the comparison of each category of the PKU dataset for various methods mentioned therein. A few of the important findings from Table 2 are summarized below. * Each of the compared methods along with our developed method yields 100% vehicle detection accuracy in the G1 and G2 categories, except the work developed in [33], whose vehicle detection accuracy is 99%. Similarly, for the G3 category, the method developed in [33,34] yields 98.20% and 99% vehicle detection accuracy. The remaining approaches all produce 100% vehicle detection results. * In the G4 category, all of the methods compared can find vehicles with an accuracy of at least 99%. In this category, YOLO-v7-based methods [46,47] yield the highest vehicle detection accuracy of 99.74% and 99.72%, respectively. While for the G5 category, an improved YOLO-v7-based method ranks first, yielding 99.22% vehicle detection accuracy. Our developed method ranks 3 rd and yields a vehicle detection result at par with [46] by delivering 99.10% detection accuracy. * On the PKU dataset to locate vehicles, an improved YOLO-v7-based method ranks first and yields a mean vehicle detection accuracy of 99.79%, followed by standard YOLO-v7 [46], whose accuracy is 99.76%. Our developed method also yields approximately similar results as compared with [46]. Vehicle detection is a prototype in our developed system.