# Towards Automatic License Plate Detection
## 4 Results and Discussion
### License Plate Localization Module (LPLM) Accuracy  
After the LPLM is applied, we observe that each license plate in the PKU dataset is contained by a green color bounding box. Therefore, we evaluate the license plate detection performance of the LPLM in terms of the localization accuracy. The discussion below mostly focuses on the accuracy of the LPLM module: \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Cat.**} & \multirow{2}{*}{**No. Vehicles**} & **Resolution (Pixels)** & **Detection Accuracy \%** & **Remarks** \\ \hline G1 & 810 & 600 \(\times\) 340 & 100 \\ G2 & 700 & 400 \(\times\) 300 & 100 \\ G3 & 743 & 420 \(\times\) 280 & 100 \\ G4 & 572 & 400 \(\times\) 320 & 99.7 \\ G5 & 1438 & 300 \(\times\) 270 & 99.1 \\ \hline \multicolumn{3}{c}{**Average**} & **99.76** & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 3: Vehicle detection accuracy. Figure 2: Detection results: first row: G1; second row: G2; third row: G3; fourth row: G4; and fifth row: G5. * Figure 2 shows the detection results of the G1-G5 categories. As shown in Figure 2, the proposed LPD algorithm accurately detects the license plates for diverse vehicle shapes and colors. In particular, all six license plates in the top row of Figure 2 are located by the LPLM. * Moreover, for severe dark contrast, as shown in the second and third rows of Figure 2, all the license plates have been detected successfully. This case is also interesting as almost all of the images shown in Figure 2 have entirely different frontal appearances. * For the G4 category, as shown in the fourth row of Figure 2, the proposed LPD algorithm effectively handles various license plates. The proposed method is robust as it is not affected by the variations in contrast and illuminations. * The G5 category in the PKU dataset contains a huge variety of vehicles, such as plates with multiple orientations and vehicles diversity. However, as seen in the bottom row of Figure 2, the proposed algorithm accurately handles the aforementioned issues of the G5 category. Table 4 summarizes the results for each of the category on the PKU dataset. From Table 4, a few of the important findings and observations are discussed below:  
* On the PKU G1-G4 categories, the proposed license plate detection method outperforms [21; 22; 23]. It is important to state here that in G1 to G4 categories the input images contain only one license plate per image under different appearances. For instance, each of these categories contain images of cars and trucks that have been captured at different times of day and night and in different sun shines. The G4 category in the PKU dataset is a slightly challenging category as out of 572 images, it contains 252 reflective glare images. However, the proposed method is efficient enough and beats all of the compared methods in terms of detection accuracy. * The G5 category in the PKU dataset is most challenging among all because it contains several vehicles and thus multiple license plates per image. Moreover, the G5 category also contains roads junctions as well crosswalks. Furthermore, it also has low and excessive luminance phenomenon, which makes it a challenging scenario for any machine learning algorithm. As seen from Table 4, in the G5 category, the proposed method ranks third among the compared methods. In this category, [23] yields the highest detection accuracy of 97.38%, whereas [21] reports 97.32% detection accuracy. * As discussed in Section 4.6 and also shown in Figure 3a, non-uniform illuminations present a major threat to the detection of a license plate in unconstrained environments. Therefore, due to the aforementioned problem, works in [24; 25] report relatively low detection accuracies of 82.37% and 83.81%, respectively. Quantitatively, the proposed method on average achieves the highest license plate detection rate of 99.04% on the whole PKU dataset (G1-G5), followed by [23], whose detection accuracy is reported to be 97.38%. The work presented in [25] reports the least accuracy of 83.81% on the PKU dataset.