# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 4 Aircraft Wake Recognition
### Ablation Experiment  
A total of four models were compared in terms of their detection performance, namely, YOLOv8n, YOLOv8n-CBAM, YOLOv8n-EfficientNetV2, and YOLOv8n-CBAM-EfficientNetV2. The comparison was made based on the precision rate, recall rate, F1-score, FPS, GFLOPs, and number of model parameters, as shown in Table 3. Figure 12a,b illustrate the comparison of the precision rate and recall rate curves for each model, respectively. Figure 12 shows that the YOLOv8n-CBAM-EfficientNetV2 model exhibits the highest precision rate on the validation set, whereas the recall rate remains relatively consistent. This evidence substantiates the feasibility of the proposed improvement to the YOLOv8n model.  
Figure 13 shows the iteration of the loss function during the experiment. As the number of iterations increases, the loss functions of the training set and validation set demonstrate a consistent convergence, indicating the absence of overfitting or underfitting. These results demonstrate that the model exhibits robust generalisation capabilities and is capable of accurately detecting previously unseen data.  
According to Table 3, by comparing the YOLOv8n with the YOLOv8n-CBAM model and the YOLOv8n-EfficientNetV2 model with the YOLOv8n-CBAM-EfficientNetV2 model, the introduction of the CBAM can improve the precision rate of the model to a certain extent. However, this approach may also result in an increase in the computational burden.  
Figure 12: Precision rate and recall rate curves for the ablation experiment. (**a**) The comparison of the precision rates of the validation set; (**b**) the comparison of the recall rates of the validation set.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **FPS** & **GFLOPs** & **Number of Parameters** \\ \hline YOLOv8n & 93.87\% & 93.71\% & 93.79\% & 161 & 8.195 & 3.006,038 \\ YOLOv8n-CBAM & 94.52\% & 93.32\% & 93.91\% & 175 & 8.209 & 3.014,601 \\ YOLOv8n-EfficientNetV2 & 95.39\% & 92.22\% & 93.78\% & 288 & 2.569 & 2,126,686 \\ YOLOv8n-CBAM- & 96.59\% & 93.58\% & 95.06\% & 250 & 2.573 & 2,135,249 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Detection Performance Parameters of Different Models in the Ablation Experiment.  
Figure 13: Loss function curves for the training set and validation set of the YOLOv8n-CBAM-EfficientNetV2 model.  
A comparison of the YOLOv8n and YOLOv8n-EfficientNetV2 models, along with their CBAM and CBAM-EfficientNetV2 variants (as detailed in this study), reveals that while the precision rate is enhanced, the model parameters and computational load are significantly reduced. This not only accelerates the convergence process but also offers a lightweight enhancement to the YOLOv8n model. Concurrently, the FPS value is enhanced from 161 to 250, thereby markedly improving the real-time performance of the model.  
Considering the precision rate and real-time performance, the YOLOv8n-CBAM-EfficientNetV2 model proposed in this study yields the most optimal detection results, exhibiting a 2.72% increase in the precision rate, a 55.28% increase in the FPS value, and a 40.78% reduction in the number of model parameters, compared with the YOLOv8n model.  
The findings substantiate that the YOLOv8n-CBAM-EfficientNetV2 model exhibits enhanced precision and real-time functionality in wake detection. The CBAM attention mechanism incorporated into the YOLOv8n-CBAM-EfficientNetV2 model improves the accuracy of the model's detection capabilities. Furthermore, the incorporation of the MB-Conv and Fused-MBConv modules into the EfficientNetV2 network results in a lightweight improvement to the YOLOv8n model.