## 5 Experimental Section
### Experimental Results
Figure 7 shows the performance comparison of the ensemble datasets and the other single datasets. Table 5 shows a comparison of the original YOLOv2 model and the proposed YOLOv2 model in terms of overall accuracy, average processing time, required GPU memory and number of floating-point operations (FLOPs) for the LPCR processes. Each model was trained with the same training dataset (Real_9k) and tested with the same test dataset (Test_13k). The original YOLOv2 model and the proposed YOLOv2 model were achieved 99.95%, 99.78% in overall accuracy, respectively. The proposed model is slightly less accurate than the original model. However, the proposed YOLOv2 model was modified the number of filters in the CNN layers, thereby the number of FLOPs was reduced from \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Dataset**} & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **6 th** & **7 th** & **Overall** \\  & **(num)** & **(num)** & **(char)** & **(num)** & **(num)** & **(num)** & **(num)** & **(num)** & **(num)** \\ \hline Web\_159 & 99.85 & 99.87 & 94.69 & 99.89 & 99.92 & 99.86 & 99.86 & 94.45 \\ \hline Real\_9k & 99.97 & 99.98 & 99.84 & 99.98 & 99.98 & 99.98 & 99.99 & 99.98 & **99.78** \\ Real\_3k & 99.96 & 99.95 & 99.80 & 99.98 & 99.98 & 99.99 & 99.98 & 99.72 \\ Real\_159 & 99.94 & 99.95 & 97.95 & 99.95 & 99.95 & 99.96 & 99.95 & 97.85 \\ \hline pix2 pix\_cGAN\_9k & 99.85 & 99.86 & 96.57 & 99.92 & 99.94 & 99.95 & 99.83 & **96.33** \\ CycleGAN\_9k & 99.35 & 99.38 & 94.97 & 99.38 & 99.56 & 99.45 & 98.98 & 93.59 \\ StarGAN\_9k & 99.43 & 99.36 & 95.21 & 99.48 & 99.45 & 99.61 & 99.45 & 94.23 \\ pix2 pix\_cGAN\_3k & 99.87 & 99.86 & 94.16 & 99.89 & 99.91 & 99.92 & 99.80 & 93.91 \\ CycleGAN\_3k & 99.13 & 99.32 & 90.56 & 99.29 & 99.46 & 99.51 & 99.13 & 89.48 \\ StarGAN\_3k & 99.51 & 99.29 & 93.25 & 99.48 & 99.56 & 99.41 & 99.17 & 92.13 \\ \hline Ensemble\_9k & 99.86 & 99.92 & 99.02 & 99.93 & 99.95 & 99.95 & 99.88 & **98.72** \\ Ensemble\_3k & 99.80 & 99.86 & 95.94 & 99.90 & 99.94 & 99.92 & 99.81 & **95.56** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of LPCR models trained with the 12 different training datasets. Figure 7: Performance comparison by the number of iterations for single LP-GANs and ensemble datasets. 29.41 billion to 7.45 billion, and the processing time and the size of required GPU memory were also reduced in half (i.e., reducing the average processing time from 22 ms to 13 ms and the size of required GPU memory from 1006 MB to 474 MB). Figure 8 shows the results of the LPCR with the modified YOLOv2 model. It is difficult for LPCR using traditional segmentation-based ALPR methods to recognize the LP images because the performance was insufficient during the LP character segmentation stage due to distortion, contamination, illumination, and noise. However, since the modified YOLOv2 model detects the LP characters end-to-end without LP character segmentation (i.e., segmentation-free), its LPCR performance on the LP images was sufficient. Nevertheless, LPCR could not recognize the LP character information for some other LP images. Figure 9 shows some failure recognition resultants by the proposed YOLOv2 model. Although the proposed YOLOv2 model is robust to various environmental weaknesses, if the LP image is severely distorted due to excessive blurring or artificial manipulation, LPCR performance is degraded.