# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 3 Proposed Method
### Deep Semantic Fusion Module  
Deep semantic information is critical for improving detection accuracy and robustness. This is particularly important in remote sensing images, where small targets are often embedded in cluttered or complex backgrounds. In such cases, shallow features, while rich in fine-grained details, may fail to capture sufficient semantic cues, making it difficult to accurately distinguish targets from the background. Deep semantic features, by contrast, provide essential category-level discrimination and global contextual understanding, effectively compensating for the limitations of low-level representations. Although SDAM efficiently captures detailed low-level features, it is essential to combine the benefits of shallow details with high-level semantics to achieve robust detection. To this end, we propose the deep semantic fusion module (DSFM), which employs a progressive fusion strategy for hierarchical feature integration. Unlike traditional fusion strategies that merge multi-scale features in a single step (e.g., FPN, BiFPN), DSFM progressively aligns and aggregates deep features in a stage-wise manner. This design ensures smooth semantic transitions across different levels of the network, effectively mitigating semantic gaps and preserving spatial coherence. In remote sensing applications, such gradual fusion is particularly beneficial, as it enables the model to build discriminative and spatially consistent feature representations, especially for small or occluded objects. Specifically, we incorporate three DSFMs in the later stages of the network's neck. These modules facilitate semantic information flow from deep layers while maintaining close interactions with SDAM outputs. This design enhances the complementarity between detailed spatial features and global semantic cues, leading to improved detection performance across various types and scales of objects. As shown in Figure 4, the two input features \(Y_{1}\),\(Y_{2}\) of the DSFM with the same dimensions, and the squeeze-and-excitation (SE) mechanism, is applied to adaptively recalibrate the channel features, enhancing the focus on important features. Specifically, global average pooling (GAP) and global max pooling (GMP) are applied to \(Y_{1}\) and \(Y_{2}\), respectively, to obtain global features along the channel dimension. The extracted features are concatenated and passed through a convolution layer to refine the channel attributes. Subsequently, an MLP module is employed to compress and then restore the features. Finally, a Sigmoid activation function then computes channel attention weights, serving to dynamically refine the original input features. The channel-enhanced features \(Y_{3}\) and \(Y_{4}\) are merged along the channel axis to form the final output \(Y\). This process can be as follows:  
\[Y_{3} =\text{SE}(Y_{1})\otimes Y_{1} \tag{6}\] \[Y_{4} =\text{SE}(Y_{2})\otimes Y_{2}\] (7) \[Y =\text{Concat}(Y3,Y4) \tag{8}\]  
where SE stands for the squeeze-and-excitation mechanism, and Concat(.) refers to concatenation along the channel dimension. After enhancing the channel features, we incorporate the spatial attention (SA) mechanism to enhance the model's capability in emphasizing target regions and strengthening the representation of spatial structural information. Unlike channel attention, which primarily contributes to global semantic modeling, spatial attention focuses on the distribution of targets across the spatial dimensions, thereby optimizing feature weighting across different regions and enhancing the model's responsiveness to key areas. Specifically, the input feature \(Y\) is processed through both mean pooling and maximum pooling, each capturing different aspects of the information effectively: average pooling primarily extracts overall background information, facilitating global distribution modeling, whereas max pooling emphasizes the most salient local features, enhancing the representation of target regions.