# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 1 Introduction  
Serving as the fundamental pillar for automated analysis of earth observation imagery, object detection in remote sensing applications has attracted considerable research interest during the past decade, emerging as a critical intersection where computer vision methodologies converge with geospatial data processing paradigms [1]. The primary objective of this task is to accurately identify and localize specific objects in remote sensing images, providing essential technical support for various real-world applications, such as disaster response [2], urban monitoring [3] and traffic management [4]. However, compared toobject detection in natural scenes, remote sensing object detection presents more complex challenges, as illustrated in Figure 1, including the diversity of scene environments, the complexity of object categories, and significant variations in object scales. In particular, high-resolution remote sensing images often contain small-sized objects with high-density distributions, which are further affected by illumination conditions, cloud cover, and atmospheric interference. These factors necessitate more robust and generalizable detection algorithms. Moreover, since remote sensing data typically encompass large-scale scenes with highly diverse object characteristics, many deep learning-based detection algorithms, despite their impressive performance in general object detection tasks, still encounter numerous challenges in complex remote sensing scenarios. Enhancing detection accuracy, model stability, and computational efficiency remains an urgent problem in this field. At present, deep neural network-driven object detectors are systematically categorized into two primary groups: anchor-based and anchor-free methods [5; 6; 7]. Anchorb-based approaches are further divided into two-stage [8; 9; 10] and one-stage detection frameworks [11; 12; 13; 14]. A well-known example of the two-stage paradigm is Faster R-CNN [10], which initially employs a region proposal network (RPN) for candidate region generation, coupled with parallel computational pathways for object categorization and bounding box coordinate optimization. While these methods achieve high detection accuracy, their computational demands are substantial, thereby constraining their applicability in time-sensitive operational scenarios. In contrast, YOLO and RetinaNet [13] represent one-stage detection methods, which directly predict object categories and bounding box coordinates on feature maps without requiring a separate region proposal step, significantly improving efficiency. YOLO leverages convolutional neural networks (CNNs) to provide end-to-end predictions, balancing speed and accuracy, whereas RetinaNet addresses class imbalance between foreground and background objects. Meanwhile, anchor-free methods such as FCOS [5] and CenterNet [7] eliminate the reliance on predefined anchor boxes and instead predict object center points, widths, heights, or bounding box coordinates directly. These techniques reduce the number of hyperparameters and improve adaptability, making them particularly effective for remote sensing object detection, where object scales vary significantly. Given the unique features of remote sensing imagery, Scholars have been actively exploring efficient multi-scale object detection and feature fusion strategies. For instance, [PERSON] et al. [15] introduced a hierarchical feature aggregation and saliency extraction architecture through multi-scale contextual integration while preserving detailed texture information through multi-scale pooling. [PERSON] et al. [16] design an adaptive feature hierarchy architecture that employs dynamic channel-spatial attention mechanisms to contextually integrate multi-resolution feature representations across spectral and spatial domains, thereby extracting more discriminative representations. Similarly, [PERSON] et al. [17] developed a global contextual fusion architecture that harvests and optimizes conceptual abstractions within  
Figure 1: Sample images from the DIOR dataset.