## 3 Results
### Experimental Results and Analyses  
The evaluation metrics for the license plate recognition network model in this study mainly included accuracy (acc), PFC (Probability of Fully Identifying the Correct License Plate Number), and Loss (value of the loss function). Among them, acc is the ratio of the number of symbols correctly recognized by the model in the prediction to the total number  
Figure 3: Sample self-built dataset. (**a**) Simulation of the effect of a license plate after sand and dust coverage; (**b**) simulating the effect of license plate under the scene of drastic change in light intensity; (**c**) example of license plate rotation effect; (**d**) simulation of the effect of a license plate obscured by sand or light.  
of symbols; by comparing the accuracy rates of different models or different datasets, the optimal model could be selected; by observing the trend of the accuracy rate during the training process, we could judge whether the model converged or overfitted; by analyzing the accuracy rate on the test set, we could evaluate the model's generalization ability. The PFC refers to the model's ability to completely recognize all the characters correctly on a license plate. It is more stringent than the accuracy rate, as PFC requires that every character on the license plate must be recognized correctly; it is usually lower than the accuracy rate because it is more sensitive to errors. Loss is a metric that measures the difference between the model's prediction and the true value; observing the trend of the loss value during the training process can determine whether the model learns effectively or not; comparing the loss value on the training and validation sets can help us detect overfitting. Here, A represents the model trained using the augmented dataset, N represents the model with an added BN layer, O represents the model trained using the unaugmented dataset, val indicates the model's performance on the test set, and train indicates the model's performance on the training set.  
In this study, we trained both the AlexNetBN model and the AlexNet model using the original dataset and the augmented dataset, respectively. After completing the training, we conducted an in-depth analysis of the performance metrics of the different models on these two datasets, aiming to validate the effectiveness of the optimization measures proposed in this study and to explore their impact on model performance.  
Figure 4 shows the performance of different models on the test set. From Figure 4, it can be observed that during the fifth and sixth epochs, the recognition accuracy of each model reached its peak. Subsequently, as the number of training epochs increased, the improvement in recognition accuracy gradually diminished and tended to converge. Furthermore, when the number of training epochs exceeded six, some models began to exhibit overfitting. The second part of this section will focus on analyzing this phenomenon. Therefore, this study will focus on the recognition performance of each model around the sixth epoch, as well as the overfitting that occurs when the number of training epochs increases, to gain a deeper understanding of the performance characteristics of the models and their potential influencing factors.  
Figure 4: Accuracy of different models in the training cycle. The content within the black dashed box is the main focus of this paper.