# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 2 Related Work  
### Feature Fusion  
In computer vision, integrating features across multiple levels and scales plays a crucial role in enhancing model performance. Early fusion techniques, such as direct concatenation or weighted summation, struggled to effectively capture complex dependencies between features. In contrast, convolutional neural network (CNN)-based multi-scale fusion methods utilize cascaded or parallel architectures to merge shallow texture details with deep semantic information, significantly improving the robustness and expressive power of the model. For example, the feature pyramid network (FPN) [22] enhances object detection across different scales by introducing a top-down fusion pathway that combines adjacent feature maps. However, this approach can lead to the gradual loss of high-resolution details in deeper layers. To address this issue, [PERSON] et al. [23] introduced a path aggregation network (PANet), which builds upon FPN by incorporating an additional bottom-up pathway. This enhancement facilitates complementary fusion between high-level semantic features and low-level spatial details, thereby improving localization accuracy. Several FPN-based extensions have been developed to further enhance feature representation. Channel-enhanced feature pyramid network (CE-FPN) [24] integrates sub-pixel residual fusion, a fine-grained context refinement module, and a channel-aware attention mechanism to mitigate aliasing effects and extract richer feature representations. [PERSON] et al. [25] proposed a gated ladder feature pyramid network (GLFPN), which gathers feature information from the backbone network at multiple scales and employs a gating mechanism to selectively fuse information from different layers, effectively reducing redundant information. To improve feature extraction in remote sensing object detection, [PERSON] et al. [26] introduced a global-to-local feature fusion network (GLF-Net), which adopts a global-to-local fusion strategy to enhance multi-scale feature utilization. [PERSON] et al. [27] developed a bidirectional dense feature pyramid network (BDFPN), which expands the scale of feature pyramids while integrating skip connections for more efficient multi-scale fusion. [PERSON] et al. [28] proposed the self-attention-guided global context feature fusion (SGCFF) strategy, which incorporates high-level contextual information across different spatial levels in FPN. Moreover, they incorporated deformable convolution and a channel-spatial attention mechanism to refine fine-grained details and mitigate spatial distortions across objects of varying scales. While these methods have explored various multi-level feature fusion techniques, they often fall short in effectively coordinating interactions between shallow and deep features. To overcome this limitation, we introduce the shallow detail aggregation module (SDAM) and the deep semantic fusion module (DSFM), which fully leverage fine-grained shallow details and high-level semantic representations. This design allows the model to capture additional learning opportunities and well-integrated feature representations.