# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 1 Introduction
(**a**) Images with complex backgrounds, (**b**,**c**) images with variations in object scales. hierarchical feature representations, mitigating the impact of complex backgrounds on foreground object detection. Moreover, [PERSON] et al. [18] introduced a scale-adaptive deformable attention architecture that constructs attention mechanisms through adaptively modulated multi-resolution perceptual domains. This module effectively captures deformable multi-scale features, making it particularly well-suited for detecting remote sensing objects of varying shapes and sizes while also improving attention accuracy. [PERSON] et al. [19] proposed an adaptive spatial feature fusion (ASFF) structure, which assigns spatial feature weights at different levels to optimize feature fusion, enhancing remote sensing object representations while minimizing feature loss. Additionally, [PERSON] et al. [20] designed a semantic fusion (SF) module that reduces semantic discrepancies between multi-scale feature maps by computing intra-feature map pixel relationships and inter-feature map correlations through matrix multiplication operations. Furthermore, [PERSON] et al. [21] introduced the full-scale object detection framework, an innovative single-phase architecture integrating a hierarchical multi-resolution feature extractor with a dimension-agnostic localization module. Despite these advancements, many existing approaches have yet to effectively address the fusion of shallow and deep features, nor have they incorporated loss functions specifically designed to handle objects of varying scales efficiently. To overcome these challenges, we present a progressive semantic-aware fusion network (ProSAF-Net), which integrates multiple specialized components to enhance detection accuracy. Given that shallow feature layers retain essential fine-grained details crucial for deep feature interactions, we introduce the shallow detail aggregation module (SDAM). This module utilizes Mamba's selective scanning and dynamic weighting mechanism to efficiently capture and refine fine details from low-level features. To ensure these details are effectively leveraged, we propose the deep semantic fusion module (DSFM), which progressively integrates and enhances fine-grained features from shallow layers with deep semantic representations. This fusion process strengthens feature discrimination and improves semantic expressiveness, contributing to more robust object detection. Furthermore, we introduce the spatial context awareness module (SCAM), designed to extract both global and local contextual information. By effectively distinguishing foreground objects from complex background noise, this module enhances detection precision. Finally, we develop an auxiliary dynamic loss function, which dynamically adjusts loss weights based on object scale variations, facilitating faster model convergence and improved detection performance across objects of different sizes. In summary, the key contributions of our work are as follows:  
* We propose ProSAF-Net, a progressive semantic-aware fusion network tailored for remote sensing object detection. This approach effectively addresses key challenges such as complex background interference and substantial variations in object scales. * To leverage the complementary advantages of fine-grained details from shallow features and high-level semantics from deep features, we design the shallow detail aggregation module (SDAM) and deep semantic fusion module (DSFM). These modules enable effective collaboration between detail extraction and semantic enhancement, improving overall detection performance in complex backgrounds. * To comprehensively exploit contextual information, we introduce a spatial context awareness module (SCAM) that filters out background noise effectively and optimizes the recognition of small targets.