## 2 Methods and Data
### New Algorithmic Ateps
#### 2.2.3 SL-YOLOv8  
As shown in Figure 4, SL-YOLOv8 (SENet_v2-LSK-YOLOv8) draws into the SENet_v2 and LSK modules in the feature detection head of the YOLOv8 model. After the output of each feature layer (P3, P4, P5), the SENet_v2 and LSK modules are embedded successively. The SENet_v2 enhances the extraction of key features by adaptively recalibrating the importance of feature channels; The LSK module improves the multi-scale adaptability of the network by dynamically adjusting the size of the receptive field. The synergy of these two modules enables SL-YOLOv8 to more effectively fuse the high-dimensional semantic information of deep feature maps with the detailed information of shallow feature maps, enhancing the detection ability of targets for different scales.  
When analyzing the computational complexity of the original YOLOv8, we divide it into two parts: the backbone network and the detection head. The backbone network mainly consists of convolutional and downsampling layers, and the computational complexity of each layer is dominated by convolution operations, assuming that the input image size is \(N\times N\), The complexity is \(O(K_{2}\cdot C_{in}\cdot C_{out}\cdot N_{2})\), \(K\) is the convolution size, and \(C_{in}\) and \(C_{out}\) are the number of input and output channels, respectively. The detection head is mainly composed of multi-scale feature layers, convolutional layers, and fully connected layers, and its computational complexity is mainly determined by the generation of multi-scale features and the final prediction. In the optimized SL-YOLOv8 network structure, SENet v2 and LSK modules are added, and its computational complexity also increases accordingly. The SENet v2 module mainly involves the global pooling and fully connected layer, and the complexity is \(O(C_{out2})\). The LSK module uses separable convolutions to increase the receptive field with a complexity of \(O(K\cdot C_{in}\cdot C_{out}\cdot N_{2})\). By comprehensively analyzing the computational complexity of the backbone network and the detection head part, the added complexity of the backbone network can be considered \(O\Big{(}\big{(}N_{2}\cdot K_{2}\cdot C_{in}\cdot C_{out}\big{)}_{original}+C_{ out2}\Big{)}\), and the added complexity of the detection header can be expressed as \(O((K\cdot C_{in}\cdot C_{out}\cdot N_{2}\cdot|HeadComplexity|)+(LSK\cdot C_{in} \cdot C_{out})+(SENet\_{v2}\cdot C_{out2}))\). Despite the additional computational complexity introduced by the new module, LSK reduces standard convolution calculations, partially offsetting the complexity of SENet v2. The overall complexity is expected to increase, but through feature enhancement and cross-layer feature aggregation, the overall feature expression and detection capabilities are improved, and the complexity is about \(O((K\cdot C_{in}\cdot C_{out}\cdot N_{2}\cdot|MainBackboneComplexity|)+C_{ out2}+LSKComplexity)\). With the introduction of the new module, the expected increase in inference time is about 10-20%  
Figure 4: Architecture diagram of the SL-YOLOv8.  
of the original YOLOv8 model, and the performance improvement is about 2-5%, possibly higher for specific tasks. In summary, by introducing the SENet v2 and LSK modules, SL-YOLOv8 greatly improves computational efficiency and detection performance through lightweight design and efficient feature expression, despite the additional computational complexity, especially in small targets and complex background tasks (8).