## 3 Methodology
### Global Localization and Context Attention  
The module consists of two sub-modules: Efficient Localization Attention (ELA) [51] for handling local context and the Global Context Block (GC) for processing global context information. As shown in Figure 2, the ELA module is responsible for capturing local salient features in the input feature maps and generating local attention weights by pooling and convolution operations in the X and Y directions, respectively, to ensure that the model can accurately focus on key regions in the image. Meanwhile, the GC module focuses on the capture and integration of global contextual information, generates a weighted feature map through matrix multiplication operations to represent the important information in the global scope, and further enhances the expressive capability of the feature map through nonlinear activation and normalization. Subsequently, the feature maps generated by the ELA and GC modules are fused by element-by-element summation to ensure the organic combination of local and global information, so that the model is able to capture detailed features while maintaining an overall grasp of the global structure. Finally, the fused feature maps are further processed by a \(1\times 1\) convolutional layer, which integrates and compresses the feature information, enhances the expressive ability of the feature maps and effectively reduces the dimensionality, thus making the subsequent computation more efficient. Figure 1: Shows the overall framework. Figure 2: Global Localization and Context Attention. Specifically, in the ELA module, firstly, the input feature maps are average pooled in the X and Y directions, respectively, which reduces the data dimensions while retaining the global information by compressing the features along specific dimensions. Then, after \(7\times 7\) convolution operation, rich local features are extracted in specific directions, followed by normalization through Group Normalization (GN) [52] to improve feature stability, and attention weights are generated through Sigmoid activation function to highlight important feature regions. Finally, the module multiplies the initial input feature map with the processed feature maps in the X- and Y-directions element by element, and uses the local attention mechanism in the spatial dimension to strengthen the salient regions in the input feature maps, so as to improve the model's ability to identify key features. In the GC module, firstly, the input feature map is transformed into two different representations, Query and Key, where the Query part performs shape transformation on the input feature map, and the Key part extracts the features through a \(1\times 1\) convolutional layer and maps them into weight distributions by SoftMax function to ensure that the significant regions are effectively attended to on a global scale. Then, through a matrix multiplication (Matmul) operation, Query and Key interact to generate a weighted feature map that represents the global contextual information in the input feature map. This weighted feature map is then fed into a network consisting of the CLR module, which enhances the representation of the feature map through nonlinear activation and normalization, and the \(1\times 1\) convolution, which further refines the feature map. Finally, the Global Context Block sums up the original input feature maps and the generated Value feature maps element by element to ensure the seamless combination of the original features and the global context information, thus improving the overall detection and recognition capability of the model. Among them, CLR firstly performs a convolution operation on \(X\) to obtain the convolution result \(W*X+b\), then performs layer normalization on the convolution result. Then, we calculate the mean \(\mu\) and standard deviation \(\sigma\), and apply the formula \(\text{LN}\ (x_{i})=\gamma_{i}\cdot\frac{x_{i}-\mu_{i}}{\sigma_{i}}+\beta_{i}\). Finally, we apply the ReLU activation function \(\text{ReLU}(x_{i})=\text{max}(0,x_{i})\).