## 3 Materials and Methods
### Convolutional Block Attention Module  
The attention mechanism fundamentally imitates humans' visual processing of images by assigning weights to different positions on the feature map to represent varying levels of attention. Remote sensing images often contain complex backgrounds, and after feature extraction through convolutional layers, there is less information about the small targets to be detected, while more information exists about the background and undetectable objects within it. These non-interest regions can interfere with the detection of small targets, leading to missed detections or false positives. In this study, we introduce the CBAM, which addresses the challenges of false positives and missed detections for small targets in remote sensing images. The CBAM combines global average pooling (GAP) and global maximum pooling (GMP) strategies to prevent information loss. To preserve more relevant target information, we incorporate the CBAM attention mechanism into our proposed model. Figure 2: Swin Transformer backbone framework. The feature map dimensions contain different types of information; the channel dimension expresses abstract features, while the spatial dimension emphasizes object position information. The CBAM architecture comprises two primary components: the Channel Attention Module (CAM) and the Spatial Attention Module (SAM). These modules are combined sequentially to generate attention feature maps in both the channel and spatial dimensions. The architecture of the Channel Attention Module is depicted in Figure 3.  
As illustrated, we can outline the CBAM procedure as follows: First, the input feature map \(F\) is subjected to adaptive feature refinement by leveraging the Channel Attention Module. This results in a modified feature map \(F^{\prime}\). Then, the Spatial Attention Module corrects the feature map \(F\). This ultimately yields the refined feature map \(F^{\prime}\) after CBAM processing. The computational procedure can be mathematically represented using the following equation, where the symbol \"\(\otimes\)\" denotes an element-wise product, the initial feature map is \(F\in R^{C\times H\times W}\), the channel-wise attention map is \(M_{\text{c}}\in R^{C\times 1\times 1}\), and the spatial-wise attention map is \(M_{\text{c}}\in R^{1\times H\times W}\):  
\[F^{\prime}=M_{\text{c}}(F)\otimes F \tag{1}\]  
\[F^{\prime}=M_{\text{c}}\big{(}F^{\prime}\big{)}\otimes F^{\prime} \tag{2}\]  
In order to compute the channel attention features effectively, a fusion of GAP and Global Maximum Pooling is utilized to reduce the spatial dimensions of the feature map. This reduction leads to the generation of two C \(\times\) 1 \(\times\) 1 feature maps. These feature maps are passed through a common Multilayer Perceptron (MLP) for subsequent processing. In this proposal, a two-layer artificial neural network is introduced. The first layer's primary aim is to reduce dimensionality on the feature maps. The layer's size, in terms of the number of neurons, is determined as C/r, where \(r\) represents the dimensionality reduction factor. Subsequently, the fusion of the two-channel attention vectors derived from the MLP takes place, followed by applying the Sigmoid activation function to obtain the final channel attention vector, denoted as \(M_{\text{c}}\). Here, \(F_{\text{avg}}^{c}\) and \(F_{\text{max}}^{c}\), respectively, represent the features obtained after applying average and max pooling operations. \[ \begin{split} M_{\text{c}}(F)&=\sigma(MLP(AvgPool(F ))+MLP(MaxPool(F)))\\ &=\sigma\Big{(}W_{1}(W_{0}(F_{\text{avg}}^{c}))+W_{1}(W_{0}(F_{ \text{max}}^{c})))\end{split}  \tag{3}\]  
To complement the Channel Attention Module, the Spatial Attention Module operates on the input feature map \(F^{\prime}\), which is obtained by the element-wise multiplication of the channel attention vector \(M_{\text{c}}\) and the input feature map \(F\).