# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 1 Introduction  
Remote sensing target detection is a technology that automatically identifies and locates specific ground objects by analyzing remote sensing images. With the improvement of remote sensing detection technology and imaging technology, its application scenarios have been continuously expanded, playing important roles in urban planning, agricultural monitoring, military reconnaissance and other fields. Significant progress has been made in this field with the integration of deep learning and high-resolution remote sensing data, attracting increasing attention [1, 2, 3, 4, 5, 6]. While most existing methods follow the paradigm offeature extraction followed by bounding box regression/target classification, differences in the backbone networks used in the feature extraction stage lead to various differences among these methods. The feature sharing mechanism is illustrated in Figure 1.  
In terms of feature extraction, approaches rooted in convolutional neural networks (CNNs) [7; 8; 9; 10; 11; 12; 13] focus on multiscale feature enhancement and contextual information extraction. For example, DetectoRS [14] combines the recursive feature interaction mechanism with a multi-scale atrous convolution fusion strategy. By constructing bidirectional feedback paths, it enables high-level semantic information to be reversely injected into low-level feature maps, thereby strengthening the feature representation capability for small targets. AODet [15] distinguishes foreground and background regions from multi-level feature maps through a foreground proposal network, operating only in foreground regions to reduce redundant computations and enhance the detection capability for targets in aerial images. Emerging transformer-based methods leverage the advantages of transformers in long-range dependency modeling to optimize target feature representation. For example, Swin Transformer [16] achieves cross-window connections through a sliding window mechanism, efficiently processing large-scale remote sensing images and enhancing the network's ability to fuse global and local information. ViTDet [17] optimizes the feature extraction capability for densely arranged small targets by introducing a pyramid vision transformer architecture and combining it with a dynamic sparse attention mechanism, which significantly reduces computational complexity while maintaining global modeling capabilities. In terms of bounding box representation, owing to the intrinsic characteristics of remote sensing targets such as multi-directional rotation and geometric elongation, directly predicting the target angle can cause the problem of discontinuous boundary in angle representation of the network loss function [18; 19; 20; 21; 22]. Therefore, GWD [23] transforms the angle prediction problem into a regression problem. Using the Gaussian weighted distance, it designs a continuously differentiable loss metric, enabling the loss function to smoothly transition at the boundary of angle representation. CSL [24] models the periodicity of angles in classification tasks by designing circular smooth labels, enabling the network to learn the cyclic characteristics of angles. Meanwhile, it uses the classification results to guide the regression task and fine-tune the angles through cosine distance metrics, which improves the accuracy and stability of bounding box representation for rotated targets. S\({}^{2}\)A-Net [25] proposes a scale-adaptive rotated bounding box representation method which decomposes the rotated bounding box into scale factors and angle parameters. By decoupling angle and scale information, it uses a multi-branch structure for separate regression. This reduces the  
Figure 1: Visualization of feature sharing. ambiguity of angle representation, effectively solving the discontinuous boundary problem and achieving accurate positioning of remote sensing rotated targets.