# Edge-AI-Based Real-Time Automated License Plate Recognition System
## 3 Experimental Results
### Real-Time Image Testing of Proposed ER-ALPR System on Actual Roads  
To verify the proposed ER-ALPR system, two experiments (one involving six time periods at one location and the other involving four time periods at two locations) were conducted to test the system's real-time image license plate recognition performance on actual roads. For the experiments, when one character on a license plate is incorrectly recognized, the entire license plate is regarded as being incorrectly recognized. In the first experiment, six time periods and a single location were selected for the testing of real-time image license plate recognition on an actual road. The weather during the six time periods was sunny. Table 4 summarizes the actual verification location, time, and number of license plates obtained. The test was carried out using the proposed ER-ALPR system, and the experimental results are presented in Table 4. The average accuracy rates under daytime and nighttime conditions were 97% and 95.5%, respectively. At night, accuracy rate was reduced because of poor lighting conditions. After the application of the proposed logical assisted judgment system method for license plate characters, the average accuracy rates under daytime and nighttime conditions increased to 98.2% and 97.3%, respectively, representing an overall accuracy rate improvement of approximately 1.5%. The experimental results indicate that the proposed ER-ALPR system is highly accurate \begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Time or Condition** & **mAP (\%)** \\ \hline Dayi Road/Guangming Road & Daytime and nighttime & 96.1 \\ Dayi Road/Guangming Road & Rain & 94.5 \\ Zhongxing Road & Daytime and nighttime & 93.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition results obtained through proposed M-YOLOv4. In the second experiment, four time periods and two locations were selected for the testing of real-time image license plate recognition on actual roads. These four time periods were characterized by rainfall of less than 40 mm/h. Because atmospheric transmittance cannot penetrate the rain screen when rainfall is more than 40 mm/h, the license plate characters captured by the camera under such rainfall conditions cannot be visually identified. Table 5 lists the test location, time, and number of license plates obtained. The proposed ER-ALPR system was used for testing, and the experimental results are presented in Table 5. Under rainy daytime conditions, the average accuracy of license plate recognition was 95.3%. Under rainy nighttime conditions, the average accuracy of license plate recognition was 93.1% because of reduced lighting conditions and rain screen interference. After the implementation of the proposed logical assisted judgment system for license plate characters, the average accuracy rates achieved under rainy daytime and rainy nighttime conditions were 96.2% and 94%, respectively, an overall improvement in accuracy rate of approximately 0.9%. The experimental results indicate that the proposed ER-ALPR system is highly accurate when performing real-time image license plate recognition on actual roads during rainy daytime and rainy nighttime conditions.