## 6 Conclusions  
In this paper, we have made improvements to the network from two perspectives: feature extraction for rotating targets and sample learning for long-tailed datasets. They were added to the baseline network LSKNet to verify their effectiveness. Firstly, feature extraction of rotating targets is a highly valued aspect of remote sensing image target detection. How to extract the features of rotating targets has been a question that researchers have been exploring. In this article, we eliminate the tedious operation of feature map rotation by transforming the convolutional kernel weights into continuous weights through bilinear interpolation, and then through a series of sampling methods. Figure 8: Qualitative comparison of different algorithms. We also extend the applicability of rotation convolution from a fixed kernel size to convolution kernels of any size and dilation rate, greatly enhancing the universality of rotation convolution, and enabling the network to dynamically optimize the extracted features. The experiment shows that our improved rotation convolution improves the accuracy of the object detection network in detecting rotating objects. Compared with the LSKNet, our proposed DOUNet improves mAP by 0.46%. In addition, to alleviate the impact of long-tail datasets on detector performance, we designed a weighted loss function based on the degree of dataset imbalance and changes in the learning state of the classifier. We fairly evaluated the degree of imbalance between each class sample based on long-term and short-term indicators, helping the classifier selectively learn tail-class data. We also designed a feature collection and generation module for tail-class data to prevent overfitting of the classifier. We use the above methods to enable the network to dynamically update the weights of tail samples and dynamically optimize the parameters of the network based on the learning situation of tail samples. The experiment shows that the improved network can effectively reduce the detection performance degradation caused by long -tail datasets, and our network improved the detection accuracy of tail categories by \(1.15\%\) compared to the baseline LSKNet. Although our work has achieved certain results in both detection accuracy and the balance of detection results, there are still some problems that need to be solved: although rotational convolution improves the accuracy of the network, it brings several-times-larger parameter quantities compared to 2D convolution, which is the disadvantage of rotational convolution. Although the idea of feature enhancement has to some extent solved the problem of overfitting of tail categories, it has not yet fully solved this problem. The accuracy improvement of tail categories is not significant enough. In future research, we will continue to explore effective solutions to these problems and design lightweight and efficient object detection models. Conceptualization, L.D. and D.Z. ; methodology, Q.L. ; software, D.Z. ; validation, D.Z., F.C. and Q.L. ; resources, L.D. ; data curation, F.C. ; writing--original draft preparation, D.Z. ; writing--review and editing, L.D. ; visualization, F.C. ; supervision, Q.L. ; project administration, L.D. ; funding acquisition, L.D. All authors have read and agreed to the published version of the manuscript. This research was funded by Key R&D Program Guidance Projects of Heilongjiang Province (Grant No. GZ20210065), 2021-2024, and Natural Science Foundation of Heilongjiang Province (Grant No. LH2019F024), China, 2019-2022. Not applicable. The datasets used in the article are all available. The DOTA dataset is available at [[https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)]([https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)), accessed on 9 September 2024.