# Towards Automatic License Plate Detection
## 4 Results and Discussion
### Vehicle Detection Accuracy  
Since the first step in the localization of license plates is to detect the position of the vehicle, we therefore apply the Faster R-CNN in the initial stage to understand the feasibility of the vehicle detection module. Figure 2 shows a few of the images with the detected vehicles by our proposed methodology. From Figure 2, a few important observations are described below:  
* As shown in top three rows of Figure 2, the vehicle detection module is intelligent. It detects all the vehicles that have the diversity in appearance. We observe that the vehicle detection module has 100% detection accuracy on G1, G2, and G3 categories. The input vehicle image resolution in these categories varies from 420 \(\times\) 280 up to 600 \(\times\) 340 pixels. Similarly, the bottom row in Figure 2 shows 13 vehicles/license plates with 100% detection result.
* Similarly, in the fourth row of Figure 2, it is quite evident that the vehicle detection module accurately locates the partially appeared vehicle in the input image. Moreover, a few of the vehicles in the fourth row have their head lights on, which generates the phenomenon of non-uniform illuminations. However, the detection module precisely detects all vehicles in the first four rows of Figure 2. The vehicle detection module has 99.7% detection accuracy on the G4 category, which has the input image resolution of 400 \(\times\) 320 pixels.
* Images shown in the fifth row of the Figure 2 are taken from G5 category, which has multiple vehicles under different test conditions. However, we see that the vehicle detection module locates all vehicles. On the PKU G5 category with input vehicle resolution of 300 \(\times\) 270 pixels, the vehicle detector module yields 99.1% detection accuracy.  
Table 3 summaries the vehicle detection results. From Table 3, important findings are listed below:  
* As can be seen in Table 3, the vehicle detection module is robust to detect diverse vehicles in the PKU dataset. For the G1-G3 categories, the vehicle detection module detects all the vehicles in the PKU dataset.
* It is evident from Table 3 that the vehicle detection module is robust to detect diverse vehicles in the PKU dataset. For the most challenging PKU-G5 category, the vehicle  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Cat.** & **Conditions** & **Input Image** & **No. of** & **No. of** & **Plates** & **Plate Height** (Pixels) \\ \hline \multirow{4}{*}{G1} & Cars on roads; ordinary & & & & \\  & environment at different & & & & \\  & daytimes; contains only & 1082 \(\times\) 728 & 810 & 810 & 35–57 \\  & one license plate per image & & & & \\  & Cars/trucks on main roads & & & & \\  & at different daytimes with & 1082 \(\times\) 728 & 700 & 700 & 30–62 \\  & sunshine; only one license & & & & \\  & plate in each image & & & & \\  & Cars/trucks on highways & & & & \\ G3 & during night; one license & 1082 \(\times\) 728 & 743 & 743 & 29–53 \\  & plate per image & & & & \\  & Cars/trucks on main roads; & & & & \\ G4 & daytimes with reflective & 1600 \(\times\) 1236 & 572 & 572 & 30–58 \\  & glare; one license plate in & & & & \\  & input images & & & & \\  & Cars/trucks at roads junctions & & & & \\ G5 & with crosswalks with several & 1600 \(\times\) 1200 & 1152 & 1438 & 20–60 \\  & plates per image & & & & \\ \hline \multicolumn{4}{c}{**The complete PKU dataset**} & **Mentioned Above** & **3977** & **4263** & **20\(\sim\)62** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the PKU dataset.  
detection module yields over 99% detection accuracy. As shown in Table 3, the vehicle detection module yields an average detection accuracy of 99.76% on the PKU dataset. This result is encouraging, as many of the vehicles in the PKU dataset are partially occluded. In addition, a large number of vehicle images become more challenging as the vehicles therein just expose the bonnet as shown in the bottom row of Figure 2. Therefore, the vehicle detection module accurately handles the aforementioned scenarios.