## 2 Improve Based on Hss
### YOLO11 Improved Based on Hss
#### 2.2.2 Integration of ShuffleAttention  
The SA (ShuffleAttention) module is primarily used to enhance deep convolutional networks in tasks such as image classification, object detection, and instance segmentation.  
Figure 3: L convolutional filters at a layer.  
By introducing an attention mechanism into the neural network, it enables the network to focus more on important features in the image while suppressing irrelevant information.  
In this study, the effect demonstrated by the SA module is excellent. In terms of the balance between efficiency and effect, the SA module effectively integrates two types of attention mechanisms. It not only maintains the light weight of the model but also significantly improves the performance of the model. In terms of parallel processing, by grouping and parallel processing the input feature maps, it can effectively reduce the consumption of computing data and accelerate the information processing speed at the same time. It also exhibits great flexibility and generalization capability. In this experiment, the YOLO11 + SA module achieved impressive results, with mAP reaching 99.2%, precision at 98.5%, and recall at 99.2%.  
ShuffleAttention is an efficient attention mechanism mainly used in convolutional neural networks (CNNs) for computer vision tasks. It enhances cross-channel information exchange by introducing a shuffle operation while maintaining computational efficiency. Figure 4 illustrates the process of the model:  
SA first performs channel grouping and channel shuffle. Channel grouping 5 partitions the channels of the input feature map into multiple groups, with the number of groups determined by the parameter \(G\). This grouping reduces computational cost while enhancing the representational capacity of the network. After the attention computation, channel shuffle is applied to rearrange the order of channels, promoting diversity and joint modeling of different features.  
Firstly, global average pooling is performed; secondly, a design linear transformation with activation is performed; and finally, the weights of the channel features are adjusted by applying the obtained channel attention weights to the corresponding feature maps using the Sigmoid activation function.  
The final output of the channel attention calculation is given by (6) and (7):  
\[s=F_{gp}(X_{k1})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}X_{k1}(i,j) \tag{6}\]  
\[X_{k1}^{\prime}=\sigma(F_{c}(s))\times X_{k1}=\sigma(W_{1  
There are also only two variable parameters, W2 and b2, which correspond to W1 and b1 in the channel attention calculation.  
Finally, feature fusion and output are performed. The final output is given by (10):  
\[X_{k}^{\prime}=\left[X_{k1}^{\prime},X_{k2}^{\prime}\right]\in\mathbb{R}^{ \mathbb{C}/G\times H\times W} \tag{10}\]  
ShuffleAttention enhances model diversity and expressive power by combining channel attention and spatial attention with the introduction of channel shuffle operations. The SA module effectively fuses the two types of attention mechanisms, which not only keeps the model lightweight, but also significantly improves its performance. In this study, the SA module played a crucial role by increasing model stability when handling complex environments and was essential for improving accuracy.