## 2 Related Work  
### Oriented Object Detection  
Generic horizontal detection utilizes horizontal bounding boxes as the localization results. Nevertheless, objects in remote sensing images exhibit characteristics such as arbitrary orientation, dense arrangement, large aspect ratios, and substantial occlusion. Using horizontal bounding boxes for object localization would introduce a significant amount of complex background, limiting the performance of the detector. Researchers have extended the generic horizontal detection paradigm to oriented object detection by introducing oriented bounding boxes. Currently, the majority of oriented object detectors adopt the overall architecture of horizontal object detectors. This is accomplished by redefining the object representation. For example, angle information is incorporated into the regression results to facilitate oriented object detection.  
The absence of feature rotation and scale invariance often limits oriented object detectors. Therefore, the design of oriented object detectors needs to consider these invariances. The two-stage detector RoI Transformer [9] proposes learning the transformation from Horizontal RoIs (HRoIs) to Rotational RoIs (RRoIs) in the first stage. In the second stage, rotation-invariant features are extracted from the rotated Region of Interest, which are subsequently used in subsequent classification and regression subtasks. ReDet [12] integrates rotation-equivariant networks into the backbone network to extract rotation-equivariant features, allowing accurate orientation prediction. In the second stage, a Rotation-invariant RoI Align operation is proposed, which adaptively extracts rotation-invariant features from equivariant features based on the orientation of the RoI.  
Achieving high-precision detection of small targets in remote-sensing images is also a challenge in the field of remote-sensing object detection. References [19; 20] designed a feature fusion module for the neck network, enhancing the detector's capability to detect tiny objects. Some scholars have focused explicitly on redefining the object representation and have achieved notable results. Oriented RepPoints [21] utilizes an adaptive point representation to achieve oriented object detection. Introducing adaptive point sets as a fine-grained representation can capture the geometric structure of aerial objects with arbitrary orientations in cluttered environments. CFA [22] proposes a convex-hull feature adaptation (CFA) approach. The CFA method establishes a convex polygon to represent objects, and then dynamically selects a set of feature points on the feature map according to the geometric shape of the object, adapting to objects with irregular layouts and orientations. Gliding Vertex [14] proposes a novel bounding box localization method by sliding the vertices of a horizontal bounding box along the corresponding edges, accurately representing objects with different orientations. Meanwhile, research on loss functions specifically suited for oriented bounding boxes has yielded substantial results. GWD [4] and KLD [5] convert rotated bounding boxes into 2-D Gaussian distributions, then compute Gaussian Wasserstein distance and Kullback-Leibler Divergence as losses, respectively. Reference [23] proposes an Adaptive Rotated Convolution (ARC) module to address the challenge of difficult feature extraction in rotated object detection due to significant changes in varying orientations. The convolutional kernels in the ARC module can adaptively rotate to extract features from objects in varying orientations, accommodating significant orientation changes of the objects within the image. LSKNet [15] proposes a selectively large kernel network suitable for remote sensing object detection. It can adaptively select spatial receptive fields, enabling better contextual information extraction.