## 3 Results
### Construction of the Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Year** & **Country** & **Amount** & **Resolution** & **Description** \\ \hline CCPD (2019) [33] & 2019 & China & 250 k & 720 \(\times\) 1280 & Unevenly bright license plates, tilted license plates, etc., but not enough of them License plates from different national regions, lack of dusty and light-variable license plates \\ \multirow{2}{*}{UFPR-ALPR [34]} & \multirow{2}{*}{2018} & \multirow{2}{*}{Brazil} & \multirow{2}{*}{4500} & \multirow{2}{*}{1920 \(\times\) 1080} & \multirow{2}{*}{1920 \(\times\) 1080} & \multirow{2}{*}{1920 \(\times\) 1080} \\  & & & & & \\ \multirow{2}{*}{GAP-LP [34]} & \multirow{2}{*}{2019} & \multirow{2}{*}{Tunisia} & \multirow{2}{*}{9175} & \multirow{2}{*}{\begin{tabular}{c} Includes multiple resolutions \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Lack of license plates with lots of dust and strong light variations; labels may be inaccurate and inconsistent \\ \end{tabular} } \\  & & & & & \\ \multirow{2}{*}{OpenALRP-EU [35]} & \multirow{2}{*}{2016} & \multirow{2}{*}{Europe} & \multirow{2}{*}{108} & \multirow{2}{*}{\begin{tabular}{c} Includes multiple resolutions \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} License plates from various EU countries with poor generalization when used \\ \end{tabular} } \\  & & & & & \\ \multirow{2}{*}{USCD-still [36]} & \multirow{2}{*}{2005} & \multirow{2}{*}{America} & \multirow{2}{*}{291} & \multirow{2}{*}{640 \(\times\) 480} & \multirow{2}{*}{\begin{tabular}{c} Multiple license plate styles and image capture conditions, fewer license plates in complex environments \\ \end{tabular} } \\  & & & & & \\ \multirow{2}{*}{CD-HARD [37]} & \multirow{2}{*}{2016} & \multirow{2}{*}{\begin{tabular}{c} Involving multiple \\ countries \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Includes multiple resolutions \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Higher number of difficult-to-identify samples, but lack of photographs of license plates that are sandy and have strong light variations \\ \end{tabular} } \\  & & & & \\ \multirow{2}{*}{CSCL} & \multirow{2}{*}{2024} & \multirow{2}{*}{China} & \multirow{2}{*}{25 k} & \multirow{2}{*}{240 \(\times\) 80} & \multirow{2}{*}{
\begin{tabular}{c} Includes a large number of license plate photos for areas with frequent dust storms and drastic changes in lighting \\ \end{tabular} } \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of license plate datasets. its recognition accuracy in real application scenarios. This enhancement allows the model to better cope with the challenges posed by changes in angles and optimizes its performance in complex scenes. * Masking: By adding a large amount of noise or increasing the local brightness in the image, some or all characters of the license plate are masked to simulate the state of the license plate in complex environments such as sand and dust coverage or direct sunlight, which further improves the robustness and adaptive ability of the model. With these data enhancement strategies, we constructed a more targeted dataset CSCL (license plates in China with a lot of sand and dust and strong changes in light variations). Some of the license plate images are shown in Figure 3. This dataset more accurately reflects the characteristics of the license plate in areas with frequent sand and dust and strong light, and significantly improves the performance of the license plate recognition model in semi-arid areas. The comparison of this dataset with the existing dataset is shown in Table 1.  
By adding Gaussian noise, salt-and-pepper noise, random noise, and adjusting the image colors, the images are made to appear as if they have more 'dust,' with a more yellowish hue, thus more realistically simulating a dust-heavy environment. The result is shown in Figure 3a. Randomly adjusting image brightness simulates conditions of strong lighting, as shown in Figure 3b.