# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 1 Introduction  
During takeoff and landing, the tail of an aircraft generates a spiral vortex with a symmetrical structure, called the aircraft wake [1]. Aircraft wake can cause potential hazards to following aircraft, limit the takeoff and landing efficiency of airlines, and even cause following aircraft to bump and roll [2, 3]. Therefore, to reduce the impact of aircraft wake on the following aircraft, it is necessary to ensure minimum wake intervals and to detect information such as the location, intensity, and duration of the wake in a timely manner so that appropriate action can be taken. The accurate identification of the wake is key to setting appropriate minimum wake interval standards to ensure that neighbouring aircraft can maintain a sufficiently safe distance to avoid the effects of the wake and to ensure flight stability and safety. Moreover, as the flight density rises steadily, particularly at busy airports and along congested routes, the study of aircraft wakes has significant implications for flight safety and efficiency. Currently, LiDAR is the main means of aircraft wake detection [4]. LiDAR can detect the radial velocity information of the atmosphere in the wind field and obtain the vortex core position, vortex circulation, and other parameters of the wake through the radial velocity and other information [5]. In recent years, some progress has been made in wake detection methods based on radial velocity. In 2015, [PERSON] used radial velocity informationcollected by LiDAR and derived wake parameters through line-of-sight velocity regression, but the error was large [6]. In 2017, [PERSON] proposed a LiDAR-based Range-Height Indicator (RHI) observation method to invert parameters such as the vortex core position and circulation [7]. In 2019, [PERSON] proposed an aircraft wake identification algorithm by extracting radial velocity spectral features [8]. In 2020, [PERSON] proposed a wake vortex core localization algorithm based on path integration, but it is computationally complex and difficult to meet real-time requirements [9]. In 2021, [PERSON] proposed a fast wake identification method based on CDL spectral width and radial wind speed, but the identification efficiency still needs improvement [10]. Machine learning and deep learning have been used in the field of aircraft wake recognitions. In 2020, [PERSON] proposed a k-nearest neighbour (KNN)-based recognition method [11], but the recognition accuracy needs to be improved. In 2021, [PERSON] proposed an aircraft wake vortex recognition method based on support vector machines (SVMs) [12], and they adopted a single velocity extreme value feature extraction method that resulted in the local information of the wake vortex being filtered out, thereby resulting in the model displaying low recognition accuracy on real measurement data. With the development of deep learning, [PERSON] classified a wake velocity cloud map based on the AlexNet network to achieve the identification of wakes in 2019, and its accuracy reached 91.30% [13]. However, the structure of the AlexNet network is complex, the number of model parameters is large, and the occupancy rate of hardware resources is high. These factors are not conducive to the real-time monitoring of wakes in airports. In 2022, [PERSON] proposed a convolutional neural network model for the rapid recognition of aircraft wakes, which was developed by improving the GoogleNet network [14]. In 2023, we proposed a method for aircraft wake recognition based on an improved ParNet convolutional neural network. The introduction of a depth-separable convolution and attention mechanism module resulted in an enhanced wake recognition accuracy. However, the real-time performance of the algorithm remains a limitation [15]. Currently, research in the field of wake detection primarily focuses on the accuracy and real-time performance of the detection algorithm, with minimal attention given to the location of the wake.