# Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model
## 3 Experiments and Results
### Experimental Process and Results Analysis  
In our research, we initially utilize the raw CCPD-Weather dataset as the foundational input for training the YOLOv5 model. This particular dataset, which has not undergone any preprocessing steps, is directly employed to facilitate the model's learning process. The objective is to assess the model's baseline performance under varying weather conditions. Following the completion of the training phase, the outcomes, including the various parameters and metrics achieved, are comprehensively detailed and visually represented in Figure 3 of our study. This illustration serves to provide a clear and concise depiction of the model's performance metrics, derived directly from the training conducted with the unprocessed CCPD-Weather dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Title** & **Releases** \\ \hline Operating System & Ubuntu18.04 \\ PyTorch & 1.9.0 \\ CUDA & 11.1 \\ Python & 3.8.10 \\ GPU & GeForce RTX 3090(24G), NVIDIA, Santa Clara, CA, USA \\ CPU & Intel(R)Xeon(R)Platinum [EMAIL_ADDRESS] GHz, Intel, Santa Clara, CA, USA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental platform and parameters. When we subjected YOLOv5 to training using the dataset in its raw, unprocessed form, the outcomes were remarkably high in terms of the model's accuracy metrics. Specifically, the precision metric reached 98.2%, indicating a high rate of true positive predictions out of all positive calls made by the model. Similarly, the recall rate stood at 98.0%, show-casing the model's efficiency in identifying true positives from the total actual positives present within the dataset. Additionally, the model demonstrated an average precision of 98.9%, which underscores its overall accuracy across different thresholds of detection. Despite these impressive metrics, our subsequent evaluation of the model, particularly when applied to the designated test set, revealed some limitations. It became apparent that, although the model performs with high accuracy, there are instances where it generates noticeable errors in the form of false positives and false negatives. This observation is clearly illustrated in Figure 3(a), where specific instances of these inaccuracies are highlighted. False positives refer to instances where the model incorrectly identifies an object as a target when it is not, whereas false negatives occur when the model fails to detect an actual target. These discrepancies, despite the high precision and recall rates, indicate areas where the model's performance can still be optimized for even greater accuracy and reliability. Figure 5 illustrates the process of processing a license plate image, showing how each image is processed individually by MAM to ensure clarity and accuracy of detection. The processed license plate images are mixed into the CCPD-Weather dataset, and the allocated dataset is then fed into the YOLOv5 model for training. The parameter results of the training are shown in Figure 6. This figure provides a comprehensive overview of the training results, highlighting the YOLOv5 model's improved capability to detect license plates accurately within the challenging conditions presented by new CCPD-Weather dataset. After training on the dataset augmented by MAM, the YOLOv5 model achieves a precision of 99.9%, an increase of 1.7%; recall of 99.1%, an increase of 1.1%; and an average precision of 99.5%, an increase of 0.6%. Testing the model on the same test set reveals significant improvements in false positives, false negatives, and confidence levels for correctly predicted instances, as depicted in Figure 3(b). Figure 3: Parameter results for training before improvement. In order to compare the superiority of the improved algorithmic model proposed in this paper to improve the effectiveness of license plate detection under strong snowfall, comparative experiments are conducted using YOLOv5, SSD, and Faster-RCNN target detection algorithms.