## 3 Methods
### SE-BackBone Network  
Due to the dense distribution of targets and small target scales in the UAV aerial image dataset, when using the baseline model YOLO v8n for target detection and recognition, the model has a weak feature extraction capability for small targets and a small detection accuracy, which cannot satisfy the detection accuracy requirements of UAV aerial images. To solve the above problems, the fixed-length feature vectors \(Q_{1}\) generated from the multi-scale input images processed by the feature pyramid pooling SPPF module are used as inputs to the SE module of the channel attention mechanism, which focuses on the information that is more critical to the current task among the many inputs and suppresses the useless information through adaptive learning of the relevance and importance of the feature channels, so as to improve the characterization ability of the network model. In this work, the channel attention mechanism SE module is applied to UAV aerial image feature extraction; as shown in Figure 3, the SE module is embedded into the BackBone  
Figure 2: LW-YOLO v8 Network Model.  
network to form a new feature extraction BackBone network, SE-BackBone, and the SE module enhances the differential information extraction of UAV aerial images by weighting the importance of the feature extracted images.  
When the SE module is introduced into the network model, the network model training can be targeted to enhance the learning of target feature regions, which helps to improve the model's detection accuracy of small targets in UAV aerial images. In the network model, the output feature map \(Q_{1}\) of the SPPF layer is passed into the SE module as the input features, which are sequentially subjected to the squeezing operation of the global average pooling (GAP) and the excitation operation composed of the two full connectivity layers (FC), and the feature maps \(Q_{2}\) outputted from the squeezing process and the channel weighted feature maps obtained from the excitation process are compared with the outputs of the squeezing process by the weighting operation one by one. The weight operation multiplies the output feature map \(Q_{3}\) from the extrusion process with the channel weighted feature map obtained from the excitation process channel by channel to re-calibrate the importance of the input features. The SE module mapping relationship is shown in Equations (1)-(3).  
\[L_{sq}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}u_{c}(i,j) \tag{1}\]  
\[L_{ex}(s,W_{0})=\theta(g(s,W_{0}))=\theta(W_{2}\varphi(W_{1}s)) \tag{2}\]  
\[L_{uv}=(u_{c},e_{c})=e_{c}u_{c} \tag{3}\]  
where \(L_{sq}\), \(L_{ex}\) and \(L_{uv}\) represent the squeeze operation, the excitation operation and the weight operation, respectively; \(H\) and \(W\) are the height and width of the input feature map \(Q_{1}\), respectively; the value of \(i\) is in the range of \([0,\,H]\), and the value of \(j\) is in the range of \([0,\,W]\); \(u_{c}\) represents each feature channel; \(s\) represents the true number after the Squeeze operation; \(\varphi\) represents the Relu nonlinear activation function; \(W_{0}\) represents the total parameters of the FC operation; \(W_{1}\), \(W_{2}\) and \(g\) represent the parameters of the FC operation, respectively; \(\theta\) represents the Sigmoid activation function performs the weight normalization operation on each feature map; and \(e_{c}\) represents the weights obtained from the Excitation operation.