# An Improved YOLO Model for Traffic Signs Small Target Image Detection
## 4 Experiment and Discussion
### Overfitting Analysis  
In the process of model design and training, overcoming overfitting is a significant challenge. Overfitting occurs when a model performs well on the training set but poorly on the test set; i.e., unseen data. This phenomenon occurs when the model memorizes the specifics (including noise and outliers) of the training data, instead of learning general rules that can be applied to unknown data, a situation referred to as overfitting. In developing our model, our primary goal was to ensure its robust performance on the training data. To  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Parameters/M** & **P\%** & **R\%** & **mAP\%** \\ \hline YOLOv5 & 7.13 & 92.9 & 86.8 & 91.8 \\ YOLOv5-Head & 14.4 & 93.3 & 89.9 & 93.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Improvement Verification Experiment on Decoupled Head.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Parameters/M** & **P\%** & **R\%** & **mAP\%** \\ \hline YOLOv5 & 7.13 & 92.9 & 86.8 & 91.8 \\ Ours & 24.3 & 95.0 & 91.6 & 95.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Validation Experiment of All Improved Algorithms.  
Figure 9: Comparative analysis of mAP curves.  
achieve this, we implemented several strategies to prevent overfitting during the training of our model using YOLOv5-v6.1.  
1. Data Augmentation: During the training phase, we employed the Mosaic data augmentation technique, which involved random cropping, rotations, scaling, and color distortions. By applying Mosaic, we were able to combine four images into a single training sample, allowing us to enhance the diversity of the training set and improve the model's generalization capabilities.
2. Dropout: Dropout was used as a regularization technique during the training process. It randomly deactivates a subset of neurons in the network during training, preventing co-adaptation and thereby reducing the risk of overfitting.
3. Early Stopping: Early stopping is an effective strategy against overfitting. By monitoring the performance on the validation set, we stopped the training process if the validation performance did not improve over several consecutive epochs. This strategy prevented the model from overfitting the training data by terminating the training process before the model could start learning noise from the training data.  
We validated our model's resistance to overfitting by monitoring its performance on both the training and validation sets throughout the training process. The model demonstrated consistent performance on both these sets, indicating its strong generalization ability. The loss curves from the training process, as shown in Figure 12, exhibit a clear convergence without any signs of overfitting.  
From the provided visualizations, several observations can be made regarding the model's performance: Both the training and validation box losses demonstrate a trend of convergence. There is no noticeable divergence between the two, which is a positive sign, indicating the absence of overfitting. The object loss (Obj Loss) showcases a similar pattern. Both training and validation losses decrease over time, with no signs of overfitting observed. The class loss (Cls Loss) also mirrors the convergence pattern seen in both the training and validation sets.  
Figure 11: Comparative analysis of recall curves.  
Figure 10: Comparative analysis of precision curves.  
In summary, the presented graphs collectively suggest that the model exhibits robust performance on both training and validation datasets, with no evident signs of overfitting.  
This consistent behavior underpins the model's generalization capabilities, which is crucial for real-world applications.