## 2 Methods and Data
### New Algorithmic Ateps  
#### 2.2.1 SENet_v2 Module  
As shown in Figure 2, SENet is an innovative architecture designed to enhance the model representation capabilities of CNNS. By explicitly modeling the dependencies  
Figure 1: Architecture diagram of the YOLOv8. between channels [30], SENet introduces the concept of an \"attention mechanism\", which achieves significant results in tasks such as image classification and target detection. The core idea of SENet is to enhance the relationship between channels represented by features in CNN through \"SE blocks\". The SE block includes two key operations: Firstly, the squeeze operation is used to conduct global pooling on the feature map in the spatial dimension to generate a global feature description, assuming that the size of the input feature map is (H \(\times\) W \(\times\) C) and the length of the feature vector obtained after pooling is \(\copyright\). The formula is as follows:  
\[Z_{c}=\frac{\sum_{i=1}^{H}\sum_{j=1}^{W}x_{ij,c}}{H\cdot W} \tag{2}\]  
where \(Z_{c}\) represents the global average pooled value of channel c, \(x_{ij,c}\) represents the pixel value of the position \((i,j)\) on channel c, H represents the height of the feature map, and W represents the width of the feature map. The excitation operation is used to perform a nonlinear transformation of the global feature vector generated by squeeze. Usually, two fully connected layers and a sigmoid activation function are used. The generated weight vector is used to reweight each channel of the original feature map. Through these two operations, SENet can dynamically adjust the weight of each channel according to the global information of the input image, thereby improving the feature representation capability of the network. SENet_v2 is an improved version of the original SENet that further optimizes the effectiveness and efficiency of the attention mechanism. In terms of a more efficient attention mechanism, SENet_v2 introduces a new computational method that makes the reweighting of channels more efficient, improving the model's efficiency by reducing computational complexity and memory usage. For the excitation operation, SENet_v2 optimizes the nonlinear transformation by adding more diverse activation functions and a deeper full-connection layer network to capture the more complex channel dependencies. The activation function in the SE block is Rectified Linear Unit (ReLU). The full-connection layer calculation is as follows:  
\[S=\sigma(W_{2}\cdot\delta(W_{1}\cdot Z)) \tag{3}\]  
where \(\delta\) represents the ReLU activation function, \(\sigma\) represents the sigmoid activation function, and \(W_{1}\) and \(W_{2}\) are the weights of the fully connected layer and the global eigenvector. In addition, the SENet_v2 features a lightweight design, primarily for use in resource-constrained mobile devices and embedded systems, employing quantization and pruning techniques that enable the model to significantly reduce compute and storage costs while maintaining performance. In the architectural design, the SENet_v2 is designed to be similar to the original SENet, but with improvements in details. The core of the SE block is still squeeze and excitation operations, but the implementation of the various stages is optimized. For example, in the squeeze operation, to reduce information loss in spa  
Figure 2: Architecture diagram of SENet_v2. tial dimensions, SENet v2 introduces Global Adaptive Pooling (GAP), which is better at capturing multi-scale features than fixed global average pooling. In the excitation phase, SENet_v2 introduces a more complex nonlinear transformation, including multiple fully connected layers and different activation functions (e.g., ReLU, Gaussian Error Linear Unit, etc.) to better adapt to different mission requirements. In addition, during the excitation phase, SENet_v2 incorporates regularization techniques, such as dropout and batch normalization, to prevent overfitting to improve model generalization.