## 4 Experiment and Analysis
### The Experiment with the Enhanced YOLO Model
#### 4.2.2 Mitigation of Model Overfitting  
In light of the results from the previous section, models employing the SiLU function demonstrated a marked superiority over those utilizing the other three functions, particularly in terms of detection speed. However, unlike the other functions, models trained with the SiLU function experienced a degree of overfitting, which compromised their generalizability. To ensure optimal usage of the function without overfitting within a certain number of training cycles, this section focuses on addressing the overfitting issue associated with the SiLU function. In the experiments, weight decay--a regularization technique--was initially employed to tackle the overfitting problem in models using the SiLU function. This method operates by incorporating a term into the model's loss function, which constitutes a portion of the sum of squares of all model weights. Minimizing this new loss function compels the model to reduce the weights towards zero, though not exactly to zero, thereby preventing the model from overly relying on the training data and enhancing its performance on new data. The hyperparameter for weight decay was adjusted from the original 0.0005 to 0.001, multiplied by the sum of the squares of the weights. Despite the increased regularization strength through the adjustment of the weight decay coefficient, the overfitting issue \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Metrics/mAP\_0.5** & **Frame per Second** \\ \hline FReLU-based & 0.74 & 44.63 \\ SiLU-based (proposed model) & 0.77 & 81.45 \\ LeakyReLU & 0.77 & 79.10 \\ MetaAconC & 0.75 & 27.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of four activation functions. Figure 6: Visualization of the training process. remained unresolved, as evident from the significant overfitting observed in the models, as illustrated in Figure 7.  
Subsequently, this section employed another regularization method--adding dropout layers--to address the overfitting issue in the model. The core concept of dropout involves randomly dropping (i.e., temporarily removing) a subset of neurons within the network during training, along with their corresponding connections. This prevents the model from overfitting the training data excessively, thereby enhancing the model's generalization ability. In practice, dropout layers randomly select a portion of neurons (determined by the dropout rate) and set their outputs to zero during the current training step. The neurons to be dropped are chosen randomly at each training step, allowing the network to learn more robust features. However, due to the random dropping of neurons, this approach may lead to the loss of useful features in convolutional neural networks, potentially resulting in decreased model performance. As shown in Figure 8, the introduction of dropout layers into the model's training process led to a decline in model performance. Although the overfitting issue was addressed, the accuracy of the model decreased by approximately 8%. Ultimately, this section addresses the issue of model overfitting by modulating the intensity of data augmentation. Adjustments were made to the parameters governing the built-in data augmentation techniques of YOLOv5, thereby varying their strength. The fundamental parameters are delineated in Table 3.  
Figure 8: Confidence function curves for training and validation sets after dropout regularization treatment. Figure 7: Confidence function curves for training and validation sets after weight decay treatment. The outcomes indicate that by escalating the level of data augmentation, the problem of overfitting was efficaciously mitigated. With robust data augmentation, the overfitting issue was essentially resolved over 300 training cycles, and a noticeable enhancement in the average accuracy of the training was also observed, as depicted in Figure 9. Furthermore, as Table 4 elucidates, in comparison to the pre-improvement SiLU function models, there was a significant augmentation in both detection precision and speed.