## 1 Introduction  
Remote sensing images, as one of the important sources of information for Earth observation and resource management, can provide accurate surface information and serve industries such as agricultural monitoring, urban planning, environmental protection, and disaster response. In recent years, object detection methods based on convolutional neural networks have brought significant breakthroughs in the utilization of remote sensing image information due to their high efficiency and ease of training [1]. How to use deep learning to complete remote sensing image object detection tasks has also become a hot research topic. Unlike natural images, in remote sensing images, due to the detected categories being severely imbalanced and the angle distribution of objects being random, it is usually necessary to use rotated rectangular boxes to accurately represent them. Due to the dense arrangement of objects, the dataset in remote sensing images exhibits an inherent long-tail distribution. The categories occupying the top are usually a few densely arranged categories, while most other categories only occupy a small part of the dataset. Derived from horizontal detection algorithms, five-parameter representation with anchor boxes is usually used for representation, such as the one-stage network Rotated RetinaNet [2] and two-stage network Rotated Faster R-CNN [3]. In addition, other networks use anchor-free box methods, such as Oriented Reppoints and Rotated Reppoints [4] based on adaptive point representation. Although these methods address the issue of representing rotating targets, they do not take into account the impact of imbalanced data on detection accuracy. This can cause convolutional neural networks to lean towards the head of the dataset, while for the tail categories of the dataset, due to insufficient learning of features, the detection accuracy will be greatly reduced. Secondly, due to the wide variety of objects detected in remote sensing images, the detected objects usually have a large-scale distribution, from larger tennis courts and football fields to smaller vehicles, different types of objects require different contextual information, and objects of various sizes require different receptive fields. This not only leads to different representation methods for objects but also results in poor performance of a series of feature extraction methods designed based on horizontal object detection. Therefore, scholars have used this idea to design many feature extraction methods, such as LSKNet [5] and PKINet [6], to customize receptive fields and feature extraction methods for different types of objects. However, these authors have noted the performance impact of target scale changes on the detection performance of the detector. They do not take into account the impact of the rotation features of the target on the detection accuracy. In addition, due to the target being detected as having a certain rotation angle, using only regular convolutions and feature fusion methods will reduce the efficiency of feature extraction. Based on this idea, convolutions that can extract rotational features were designed, such as ARCNet [7], S\({}^{2}\)A-Net [8], ReDet [9], etc. They did not consider the different receptive fields required to detect targets of different scales and categories. Summarizing the research content of previous scholars, we found that some factors still affect the accuracy of the detection process on remote sensing images, and based on this, we designed corresponding methods to solve the following problems:  
1. Previous research on feature extraction and fusion of rotating targets was not sufficiently comprehensive. In response to this issue, we combined previous research results and added both large kernel convolution and rotation convolution to the network, modified the backbone network and feature fusion network, and designed an effective feature extraction method. 2. Previous studies did not take the impact of dataset imbalance on detection accuracy into account.