# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 4 Aircraft Wake Recognition  
### Aircraft Wake Image Dataset  
The one-dimensional echo data collected by the LiDAR are preprocessed as follows. First, the radial distance of the LiDAR scan is employed as the horizontal coordinate, with the radar elevation angle serving as the vertical coordinate. Subsequently, the radial velocity data of the discrete points within the wind field data are mapped into the coordinate system through the utilisation of the bicubic interpolation algorithm [30]. Finally, the wind velocity values of each interpolated point are mapped into the coordinate system through the application of the jet colour mapping scheme, thereby facilitating the generation of velocity cloud maps for the two-dimensional wind field cross-section, as illustrated in Figure 7. Moreover, Figure 7 shows a sample of the detected wake data at 4:35:31 on 5 February 2019. The horizontal coordinate represents the radial distance detected by the LiDAR, whereas the vertical coordinate depicts the pitch angle of the LiDAR. The symmetrical structure depicted in the figure represents the aircraft wake. In Figure 7 the red color denotes the large value of the wind speed, while the blue color denotes the small value of the speed.  
Positive speed means the wind speed has the same direction with the laser beam, while negative speed indicates the wind speed has the opposite direction.  
The dataset employed in this experiment for wake detection comprised samples of wake data detected at the Hong Kong International Airport between 3 and 5 February 2019. A total of 5320 LiDAR scanned images were obtained for the experiment, comprising 4000 samples with wake and 1000 samples without wake. In the course of the image screening process, 152 images displaying burr effects and 168 images entirely obscured by background wind were identified and excluded. In this experiment, the dataset was divided into a training set and validation set by randomly assigning 3000 images of wake and 2000 images of no wake in a ratio of 7:3. Figure 8a shows the positive samples, which exhibit the presence of wake, whereas Figure 8b illustrates the negative samples, which lack wake. The horizontal coordinate represents the distance detected by the LiDAR, which ranges from 1000 to 1780 m, whereas the vertical coordinate represents the elevation angle of the emitted laser of the LiDAR, which ranges from \(-0.125^{\circ}\) to \(5^{\circ}\).  
Figure 8: Positive and negative samples in the dataset. (**a**) Positive samples with wake; (**b**) negative samples without wake.  
Figure 7: Wake structure under LiDAR RHI scanning.  
Prior to the commencement of the wake detection experiment, it is necessary to preprocess the dataset images. This involves adjusting the resolution of each sample in the wake image dataset to 316 \(\times\) 144 pixels and then performing grayscale processing on each sample. Figure 9 illustrates the latter. Figure 10 presents the final generated samples of the dataset. Figure 10a-d illustrate the wake samples under four distinct scenarios: upwind, clear, downwind, and left-right vortex separation, respectively.  
Following the completion of the image processing, it is essential to create labels for the wakes within the images. This is achieved through the utilisation of LabelIMG, which is a target label creation tool that allows for the annotation of each wake sample with wake features. Thus, LabelIMG generates the requisite sample datasets of wakes with labels. To facilitate the convenient representation of the left and right vortices of the wake, the labels LV and RV were employed to distinguish between the left vortex and right vortex, respectively. Figure 11 presents the labelled dataset samples.  
Figure 11: Dataset samples labelled via LabelIMG. (**a**) normal vortex with upwind; (**b**) normal vortex with clear sky; (**c**) normal vortex with downwind; (**d**) separate vortex with clear sky.  
Figure 9: Grayscale processing of wake images.