# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 5 Conclusions  
This study proposes an aircraft wake detection method based on YOLOv8n-CBAM-EfficientNetV2. The left and right wake vortices in the LiDAR RHI scanning image were labelled to generate the wake dataset. The CBAM was introduced into the YOLOv8n-CBAM-EfficientNetV2 model and enhanced the model's capacity to extract spatial features, thereby improving its detection accuracy. The incorporation of the EfficientNetV2 network submodule (MBConv and Fused-MBConv modules) resulted in a notable reduction in the model's complexity, a decrease in the number of model parameters, and an enhancement in the model's real-time performance. The results of the ablation experiment demonstrate that the incorporation of the CBAM and EfficientNetV2 module markedly improves the detection performance of the model. The YOLOv8n-CBAM-EfficientNetV2 model demonstrates a precision rate of 96.59%, representing a 2.72% improvement; a 40.78% reduction in the number of model parameters; and a 55.28% increase in frames per second (FPSs), compared with the YOLOv8n algorithm. Additional comparative experimental results demonstrate that the YOLOv8n-CBAM-EfficientNetV2 model exhibits optimal performance in all aspects of aircraft wake detection when compared with conventional machine learning algorithms and other target detection algorithms. This is evidenced by its superior recognition effectiveness and the reduced hardware and software costs required, which sub \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **FPS** & **GFLOPs** & **Number of Parameters** \\ \hline KNN & 92.31\% & 89.55\% & 86.15\% & - & - & - \\ SVM & 88.41\% & 93.85\% & 91.04\% & - & - & - \\ ParNet & 92.64\% & 96.92\% & 94.73\% & 67 & 12.386 & 7/21,645 \\ SSD & 88.14\% & 89.27\% & 88.70\% & 41 & 60.807 & 23,612,246 \\ YOLOv5-lite & 93.70\% & 94.26\% & 93.98\% & 208 & 7.178 & 2,508,854 \\ YOLOv8n & 93.87\% & 93.71\% & 93.79\% & 161 & 8.195 & 3,006,038 \\ YOLOv8n-CBAM-EfficientNetV2 & 96.59\% & 93.58\% & 95.06\% & 250 & 2.573 & 2,135,249 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative Experiment of Wake Detection Performance. stantiates the viability of utilising a deep learning model for wake detection. Furthermore, the lightweight nature of the model allows for its deployment on embedded devices. The limited dataset is not representative of the wake conditions that may occur in certain extreme environments. While the probability of wake over the runway in extremely adverse weather conditions is low, such conditions may have a significant impact on aircraft takeoffs and landings at airports. Therefore, this phenomenon warrants further investigation. Meanwhile, as the wake develops and dissipates, the left and right vortices become separated, and the vortex circulation declines. This process presents a significant challenge to the detection capability of the model, potentially leading to missed detections. For this reason, this research group will conduct further simulations of the wake field in extreme weather conditions and its subsequent decay to improve the wake detection performance of the model. Furthermore, the set operational mode of the LiDAR influences the diversity of the data collected. Additionally, the range of sampling angles, distance from the runway, and location of the LiDAR all impact the distribution of the collected wake within the entire wind field. Therefore, it is possible to consider the joint detection of wake by multiple LiDARs, which would provide a more comprehensive picture of the airport environment to air traffic controllers. Conceptualisation, Y.M. ; methodology, Y.M. and X.T. ; software, X.T. and Y.S. ; supervision, P.-W.C. All authors have read and agreed to the published version of the manuscript. This work was funded by National Natural Science Foundation of China, grant number U1833111. Not applicable. The data used to support the findings of the study are available from the corresponding author upon request. The authors declare no conflicts of interest.