# Towards Automatic License Plate Detection
## 4 Results and Discussion
### Evaluation on Extreme Reflective Glare, Low, and High Luminance  
To further evaluate the robustness of proposed method as described in Algorithm 2, we test our algorithm on extreme reflective glare conditions as described in Figure 3a,b. In this study, we investigate three challenges. First is the extreme reflective glare scenario, as shown in Figure 3a. The second challenge is low-luminance, as depicted in top row of Figure 3b, and the final challenge is the excessive luminance condition as shown in bottom row of Figure 3b. For each of these challenges, our important observations are highlighted below:  
* For the extreme reflective glare scenario, as shown in top row of Figure 3a, only the vehicle is detected but not the license plate. However, after the proposed reflective  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **G1** & **G2** & **G3** & **G4** & **G5** & **Average** \\ \hline
[21] & 98.76 & 98.42 & 97.72 & 96.23 & 97.32 & 96.62 \\
[22] & 98.89 & 98.42 & 95.83 & 81.17 & 83.31 & 91.09 \\
[23] & 97.39 & 97.30 & 97.45 & 97.38 & 97.38 & 97.38 \\
[24] & 95.43 & 97.85 & 94.21 & 81.23 & 82.37 & 91.09 \\
[25] & 82.90 & 83.30 & 87.11 & 83.71 & 83.81 & 84.16 \\ \hline
**Proposed** & **99.81** & **99.50** & **99.20** & **99.40** & **97.30** & **99.04** \\ \hline \hline \end{tabular}
\end{table}
Table 4: License plate detection accuracy (%) comparison.  
Figure 3: Detection results on **(a)** PKU-G5 extreme reflective glare and **(b)** top-row: low luminance and bottom-row: excessive luminance.  
glare mechanism is applied, it is evident in the third image of the top row in Figure 3a that the license plate is accurately detected. Moreover, the quality of the image is now improved with a much more pleasant outlook. Similarly, the second row in Figure 3a is even more challenging phenomenon in which the color of the vehicle is also white along with the reflective glare. In such conditions, only the vehicle is detected. After we apply the glare rectification mechanism, it can be seen in second and third images of Figure 3a that the image quality has improved along with the accurate detection of the license plate.
* For the low-luminance scenario, as shown in the top row of Figure 3b, only the vehicle is detected. However, after the proposed luminance rectification method is applied, it is evident in the third image of the top row in Figure 3b that the license plate is now precisely detected.
* For the excessive-luminance scenario, as shown in second row of Figure 3b, only the vehicle is detected. In this image, the bonnet and the license plate area are barely differentiable through naked eye. As can be seen after the proposed luminance rectification method is applied, the license plate is exactly detected.
* To the best of our knowledge, none of the previous works [21; 22; 23; 24; 25] address reflective glare scenarios. It is important to state here that the vehicle detection module is robust and precisely locates vehicles under severe dark or over bright contrast. The images shown in Figure 3b depict the bad lighting conditions scenario. However, the proposed method handles the aforesaid scenario efficiently.
* It is important to state here that such a reflective scenario is found only in the G4 and G5 categories. Out of the 572 test images from the G4 category, we found and analyzed 252 test images to be affected by the reflective glare. Meanwhile, from the 1152 test images of the G5 category, we found 706 images to be either low/high luminance and affected by extreme reflective glare. In our simulations, we have tested all such images, and our findings are summarized in Table 4. Due to these variations, the published works in [22; 24; 25] report much lower detection accuracy in the G5 category. The works in [21; 23] report slightly higher detection accuracy in the G5 category than the proposed method. However, the proposed method outperforms all works published in [21; 22; 23; 24; 25] in terms of the mean accuracy on all five categories of the PKU vehicle license plate dataset.