# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 3 Methods
### Introduction of DGBL-YOLOv8 Object Detection Algorithm
#### 3.1.2 Structure of the GLBA-BiFPN  
The neck structure of YOLOv8 is designed using a combination of a Feature Pyramid Network (FPN) [23] and a Path Aggregation Network (PAN) [24]. While FPN enhances feature fusion through a top-down pathway, it neglects the reverse transmission of fine-grained shallow features, limiting its ability to capture small object details. PAN, although incorporating a bottom-up pathway, relies on fixed-weight feature fusion, making it unable to dynamically adjust the contributions of different feature levels based on task requirements. To address these limitations, researchers proposed the Bidirectional Feature Pyramid Network (BiFPN), which integrates bidirectional feature fusion, weighted fusion mechanisms, and a simplified structure to enhance feature information integration across different levels. This enhances both the speed and precision of object detection algorithms. However, when dealing with small-scale, densely distributed, and distant objects, BiFPN's use of standard convolution exhibits limited capability in feature extraction and contextual information fusion. To overcome this issue, this study builds upon the BiFPN architecture by introducing the Global-to-Local Spatial Aggregation (GLSA) module, which optimizes standard convolution to enhance feature extraction and information fusion. The resulting GLSA-BiFPN \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Computation** \\ \hline Standard Convolution & \(I_{in}\times I_{out}\times m\times m\) & \(P\times Q\times I_{in}\times I_{out}\times m\times m\) \\ Depth Convolution & \(I_{in}\times m\times m\) & \(P\times Q\times I_{in}\times m\times m\) \\ Point-wise Convolution & \(I_{in}\times I_{out}\) & \(I_{in}\times I_{out}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Formulas for convolution parameters and computational complexity. structure improves small object detection performance. The architecture of GLSA-BiFPN is illustrated in Figure 4.  
The Global-to-Local Spatial Aggregation (GLSA) module consists of two significant components: the Global Spatial Attention (GSA) module and the Local Spatial Attention (LSA) module. These modules work together to enhance feature representation by integrating both global and local spatial information. The architecture of the GLSA module is illustrated in Figure 5.  
Specifically, the input feature map is first evenly split into two equal parts along the channel dimension. And its number of channels is 64. Each subset of feature maps is independently processed through the Global Spatial Attention (GSA) module and the Local Spatial Attention (LSA) module. Subsequently, the outputs are combined by a \(1\times 1\) convolutional layer along the channel dimension to generate the final feature map. The corresponding mathematical formulations are presented in Equations (2) and (3):  
\[A_{i}^{1},A_{i}^{2}=Split(A_{i}) \tag{2}\]  
\[A_{i}^{\prime}=C_{1\times 1}\Big{(}Concat\Big{(}X_{sa}\Big{(}A_{i}^{1}\Big{)},Y_ {sa}\Big{(}A_{i}^{2}\Big{)}\Big{)}\Big{)} \tag{3}\]  
In the equations, \(Xsa\) and \(Ysa\) represent the Global Spatial Attention and Local Spatial Attention, respectively. \(A_{i}\) represents the feature map with 64 channels, where \(A_{i}\) is denoted by {\(A_{i}\,\)i\(\in\)(2, 3, 4)}, \(A_{i}^{1}\) and \(A_{i}^{2}\) represent denote two 32-channel feature maps {\(A_{i}^{1},A_{i}^{2}\,\)i\(\in\)(2, 3, 4)}, respectively, \(A_{i}^{\prime}\) represents the output feature. Figure 4: Architecture of the GLBA-BiFPN. Figure 5: Architecture of the GLSA module. The Global Spatial Attention (GSA) module focuses on capturing long-range relationships between pixels in the spatial domain, serving as a complement to Local Spatial Attention (LSA).