## 1 Introduction  
Aerial image object detection involves analyzing images captured by satellites or drones to locate and classify specific targets, and is an important application in the field of computer vision.  
With the continuous improvements in computing power, traditional image recognition algorithms relying on manual feature extraction are becoming increasingly inadequate for detecting targets in remote sensing images. Compared with traditional object detection methods, object detection based on convolutional neural networks is more robust and less affected by image lighting or resolution. This approach enhances detection efficiency and accuracy, enabling automated target detection in aerial images.  
Many challenges are encountered in object detection using aerial images. For example, the pixels of targets that need to be detected in the image, such as cars and bridges, are relatively small, usually less than 20 \(Pix\times 20\)\(Pix\), and they are often densely arranged, making identification difficult.  
Since the use of horizontal object detection boxes causes adjacent targets to overlap during detection, it is difficult for general object detection models such as YOLO [1] and Faster-RCNN [2] to solve dense detection and skewed detection problems.  
The dense oriented object detection system based on convolutional neural networks exhibits excellent recognition performance. Convolutional neural networks possess strong feature extraction capabilities, enabling the extraction of feature information from a large number of densely arranged targets and facilitating corresponding weight adjustments.  
The main research contributions of this paper are as follows:  
1. Feature fusion network design based on automated structure search The FAS module is incorporated into the Inception-ResNet feature extraction network for general object detection. This module enables automatic identification of optimal feature fusion paths, mitigating content loss in large-scale, dense rotated targets during multi-layer feature extraction and overcoming the limitations of manually designed fusion paths. Additionally, by designing the search space and search strategy, the proposed method resolves the issue of wide-ranging architecture search methods being infeasible on a single GPU.
2. Design of an anchor-free decoupled detection head for oriented object detection For dense rotated targets, which are often non-horizontally arranged, traditional Region Proposal Network (RPN) approaches exhibit low detection efficiency and often include multiple targets within a single candidate box, resulting in poor performance. This paper proposes a decoupled detection head (DDH) without anchors, which decouples the processes of target bounding box regression and target classification. This allows different convolutional branches to learn distinct feature emphases. Furthermore, a centrality branch is introduced on top of the decoupling mechanism to enhance the precision of bounding box regression and improve recognition speed.
3. Ellipse center sampling method To address the issue of low sampling rates in horizontal center sampling for detecting rotated targets with large aspect ratios, this study proposes an improved ellipse center sampling (ECS) method. The ECS method increases the sampling area of rotated detection boxes, reducing the difficulty of network training and improving object detection accuracy. Additionally, it converts rotated rectangles into a distance representation based on an elliptical Gaussian distribution, resolving the challenge of computing gradients for intersection over union (IoU) loss at arbitrary angles. Experiments on the DOTA [3] and HRSC2016 [4] datasets validate the feasibility of the proposed method. Compared to improved second-order methods [5; 6; 7; 8], the proposed approach not only employs an anchor-free detection head but also requires only convolutional layers due to internal decoupling, making it more easily deployable on most edge computing platforms.