# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 1 Introduction
In summary, although existing methods have optimized multi-scale feature fusion capabilities to a certain extent and alleviated the problem of inaccurate angle prediction in rotating target detection, they overlook the impact of gradual changes in target scale during the feature extraction process of the detector on the diversity of target orientations. In detail:  
1. Progressive Scale Variation: In the same scene, targets of different sizes (such as various ships of different sizes) exhibit continuous progressive changes; this progressive variation in scale poses significant challenges to traditional target detection algorithms, as detectors with fixed-scale designs often struggle to accurately detect targets of all sizes simultaneously. To address the issues caused by scale variation, existing methods commonly use multi-level structures such as feature pyramids for feature fusion. Although increasing the number of stacked layers can cover a wider scale range to some extent, this significantly increases computational complexity; conversely, reducing the number of layers compromises detection accuracy. Consequently, existing methodologies face substantial challenges in striking an optimal balance between detection accuracy and computational efficiency. Against this backdrop, LSKNet [26] proposes a method aimed at selectively expanding the spatial receptive field for large-scale targets to capture more scene contextual information. This is achieved by introducing large-kernel convolutions and dilated convolutions into the backbone network. However, large-kernel convolutions may introduce substantial background noise, which negatively impacts the precise detection of small targets. On the other hand, while dilated convolutions have advantages in expanding the receptive field, they may overlook fine-grained information within the receptive field, which can lead to overly sparse feature representations and affect detection accuracy. 2. Coupling of Scale and Orientation: In remote sensing target detection tasks, existing methods typically model scale variation or orientation variation independently, while overlooking the deep coupling relationship between the two. When the target size is large, the features extracted within the target often fail to fully cover the entire target due to the limited local receptive field of convolutional operations, resulting in the absence of critical directional information in local features. This phenomenon makes it difficult to accurately capture the target's orientation and shape structure by relying solely on local scale features, thereby affecting the accuracy of target detection. Therefore, the increase in target scale not only exacerbates the locality problem of feature extraction but also indirectly weakens the orientation perception ability. To effectively address this challenge, it is necessary to first perform fine-grained scale modeling during feature extraction in order to ensure that local features have sufficient global perception capabilities. Subsequently, by integrating an orientation modeling mechanism, the method can fully exploit the expressive ability of target orientation features under the background of scale variation, thereby achieving collaborative perception and enhancement of scale and orientation and improving the adaptability and robustness of detectors in complex remote sensing scenarios. To address these issues, we propose MIHE-Net. In the feature extraction stage, we introduce the MCI module and HEA module, which utilize a set of parallel deep convolutions and depth-wise separable strip convolutions to simultaneously extract local and global features of remote sensing targets. The obtained feature maps are then fed into subsequent parameter regression branches. In the parameter regression stage, we propose a midpoint offset method to represent oriented targets and design a new loss function for bounding box regression, helping to address the problem of feature coupling between target scale and orientation in existing object detection algorithms.