# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 5 Conclusions  
In the task of remote sensing object detection, it is necessary to address the inability of feature pyramid structures with fixed-scale design in the backbone network to adapt to progressive scale changes of objects, as well as the problem that relying solely on local-scale features makes it difficult for the detector to accurately capture object orientations. To solve these issues, we propose a Rotation Target Detection Network Based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net). The proposed method extracts local features of objects through the proposed MCI module, ensures that local features have sufficient global perception capability via the proposed HEA module, and achieves precise object regression using the proposed Midpoint Offset Loss Function. Comparative experiments with other advanced detectors show that the resulting detector not only achieves excellent detection performance but also efficiently balances computational complexity and detection accuracy.  
MIHE-Net demonstrates significant practical application value in remote sensing target detection, particularly due to its excellent ability to handle targets with progressive scale changes and multi-directional orientations. In military reconnaissance, it can efficiently capture the gradual scale changes of targets such as ships and aircraft (from far to near or small to large) and accurately locate moving targets with different heading angles,  
Figure 7: Failure cases caused by occlusion, extreme aspect ratios, and background clutter.  
ensuring stable detection in complex scenarios. In agriculture, it can precisely capture plot boundaries with different orientations, such as diagonal ridges and irregular plot contours, enabling high-precision identification of crop types and plot information. However, there remains room for improvement in scenarios involving severely occluded targets and targets with extreme aspect ratios. In future research, the global context modeling capability of the HEA module could be further enhanced to capture long-range dependencies in dense scenes; alternatively, an occlusion-aware mechanism could be introduced to improve the feature integrity of partially occluded targets. Additionally, applications in synthetic aperture radar (SAR) could be explored by optimizing the feature extraction strategy of the MCI module to deal with speckle noise in SAR images.  
G.X. and D.J.: experimental design, data collection, data analysis, interpretation of results, drafting the original manuscript; Q.W.: critical guidance and supervision to ensure the quality and direction of the research. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The links to obtain the research data are [[https://github.com/ming71/HIRSC2016_SOTA](https://github.com/ming71/HIRSC2016_SOTA)]([https://github.com/ming71/HIRSC2016_SOTA](https://github.com/ming71/HIRSC2016_SOTA)) and [[https://github.com/ming71/UCAS-AOD-benchmark](https://github.com/ming71/UCAS-AOD-benchmark)]([https://github.com/ming71/UCAS-AOD-benchmark](https://github.com/ming71/UCAS-AOD-benchmark)) (accessed on 3 June 2025).  
The authors declare no conflicts of interest.