## 3 Results and Discussion
### License Plate Detection Comparison with Other Methods  
In this section, a comparison is made with already existing license plate detection algorithms. The efficiency of the two-step license plate identification algorithm is presented  
Figure 9: Results of two-step identification algorithm for the AOLP database: (**a**) precision scores; (**b**) recall scores; (**c**) F1 scores; (**d**) Ptl point error scores; (**e**) Pt2 point error scores. and analyzed with different datasets. The speed for single-image processing and the speed for the neural network were calculated and compared to other methods. License plate detection methods in [31; 32] require a vehicle detection step, making the overall license plate detection process slower. Table 2 shows the speed given as a sum of time required to detect the vehicle (YOLOv2) and the time required to detect the license plate (Fast-YOLOv2). Experiments were also performed with YOLOv3 and Fast-YOLOv3, but their performance is better on small objects. [PERSON] et al. 's research proposes a flow-guided spatiotemporal attention detection network to detect license plates in complex situations and track them [20]. The UFPR-ALPR dataset, consisting of 4500 images from 150 vehicles in real-world environments, was used during the experiments. Although precision and recall were better in all the discussed papers, they were significantly slower than the proposed license plate detection algorithm. Results from the AOLP dataset showed that the proposed license plate detection algorithm's precision and recall yield better results than [21] or [18], but worse than [32]. The AOLP dataset can be further divided into three subsets (AC, LE, and RP), and results vary for each, so average precision is taken into consideration. In [21], results show a precision of 97.70% with a recognition speed of more than 21 FPS. In [18], the average precision was 95.50% (AC--92.6%, LE--93.5%, RP--92.9%). It is worth noting that the network structures used can affect precision. Both [18; 21], not being the most novel solutions, result in worse performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **Speed (ms\(\,\)l\(\,\)fps)** \\ \hline \multicolumn{4}{c}{UFPR-ALPR dataset} \\ \hline [PERSON] et al. [31] & 98.33 & 97.33 & 11.16\(\,\)i\(\,\)90 + 4.07\(\,\)i\(\,\)246 \\ [PERSON] et al. [32] & 98.67 & 98.67 & 8.54\(\,\)i\(\,\)117 + 3.09\(\,\)i\(\,\)324 \\ [PERSON] et al. [20] & 99.11 & 99.39 & 122\(\,\)i\(\,\)- \\ Proposed & 96.19 & 85.61 & 2.2468\(\,\)i\(\,\)444 + 0.4686\(\,\)i\(\,\)2133 \\ \hline \multicolumn{4}{c}{AOLP dataset} \\ \hline [PERSON] et al. [32] & 99.85 & 99.85 & 8.54\(\,\)i\(\,\)117 + 3.09\(\,\)i\(\,\)324 \\ [PERSON] et al. [21] & 95.92 & 97.60 & -\(\,\)i\(\,\)62.93 \\ [PERSON] et al. [18] & 95.50 & 95.43 & -\(\,\)i\(\,\)- \\ Proposed & 96.19 & 100 & 2.2468\(\,\)i\(\,\)444 + 0.4686\(\,\)i\(\,\)2133 \\ \hline \multicolumn{4}{c}{OpenALPR dataset} \\ \hline [PERSON] et al. [32] & 98.52 & 98.52 & 8.54\(\,\)i\(\,\)117 + 3.09\(\,\)i\(\,\)324 \\ [PERSON]. [PERSON], [PERSON] [28] & 90.94 & - & -\(\,\)i\(\,\)- \\ Proposed & 96.19 & 96.17 & \\ \hline \multicolumn{4}{c}{CaltechCars} \\ \hline [PERSON] et al. [32] & 99.13 & 99.13 & 8.54\(\,\)i\(\,\)117 + 3.09\(\,\)i\(\,\)324 \\ [PERSON] et al. [32] & 93.80 & 91.30 & -\(\,\)i\(\,\)- \\ [PERSON] et al. [19] & 98.39 & 96.83 & -\(\,\)i\(\,\)- \\ Proposed & 96.19 & 99.19 & 2.2468\(\,\)i\(\,\)444 + 0.4686\(\,\)i\(\,\)2133 \\ \hline \multicolumn{4}{c}{EnglishLP} \\ \hline [PERSON] et al. [32] & 100 & 100 & 8.54\(\,\)i\(\,\)117 + 3.09\(\,\)i\(\,\)324 \\ Proposed & 96.19 & 100 & 2.2468\(\,\)i\(\,\)444 + 0.4686\(\,\)i\(\,\)2133 \\ \hline \multicolumn{4}{c}{Other} \\ \hline Kernel density [23] & 98.1 & - & 2.71\(\,\)i\(\,\)452 \\ \hline STELLA [24] & 97.9 & 96.3 & -\(\,\)i\(\,\)30 \\ \hline Bounding ellipses [25] & - & - & -\(\,\)i\(\,\)38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection performance comparisons for various datasets.