## 4 Design of Decoupled Detection Head for Elliptical Center Sampling  
After feature extraction and fusion, the resulting feature maps are fed into the detection head for object recognition, which involves delineating object detection boxes and classifying the images within these boxes.  
In object detection tasks, localization and classification have different focuses. Classification emphasizes the texture information of the target, as shown in Figure 3a, while localization focuses more on the edge information of the target, as shown in Figure 3b.  
In the YOLO-series models, the classification and bounding box regression in the detection head share weights, utilizing the same set of feature maps for output.  
To decouple the two tasks with different focuses, this paper proposes a decoupled object detection head. Leveraging the spatio-temporal sensitivity of fully connected layers [23], these are utilized for foreground class classification, while convolutional layers, which effectively learn the characteristics of the target foreground and background, are employed for bounding box regression. This ensures that the two tasks no longer share weights. Additionally, a centrality branch is introduced in the detection head to further reduce the difficulty of bounding box regression.  
It is difficult for general object detection methods with four-dimensional detection heads to efficiently and accurately detect targets in dense rotated images due to their dense arrangement and arbitrary angles. The detection performance of horizontal detection heads is shown in Figure 4. It can be observed that for densely arranged small targets, detection fails when the tilt angle exceeds a certain threshold, making it impossible to detect closely  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\
**AP@IOU = 0.7 (\%)** & & & & \\ \hline Inception & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ ResNet101 & - & \(\surd\) & \(\surd\) & \(\surd\) \\ SPP & - & - & \(\surd\) & \(\surd\) \\ FAS & - & - & - & \(\surd\) \\ \hline Plane & 88.6 & 89.6 & 89.7 & **90.5** \\ Baseball diamond & 76.8 & 85.2 & 85.3 & **87.1** \\ Bridge & 54.6 & 57.5 & 62.4 & **62.5** \\ Ground track field & 69.2 & 70.5 & 74.2 & **82.1** \\ Small vehicle & 78.1 & 71.7 & 77.6 & **78.5** \\ Large vehicle & 77.7 & 77.6 & 81.1 & **82.7** \\ Ship & 87 & 78.1 & **88.3** & 87.2 \\ Tennis court & 91 & 91 & **91.5** & 91 \\ Basketball court & 84 & 85.1 & 83.6 & **88.7** \\ Storage tank & 83.6 & 85.5 & 86.1 & **87.2** \\ Soccer ball field & 58.7 & 67.5 & 68.8 & **69.6** \\ Roundabout & 65.7 & 61.6 & 67.6 & **68.9** \\ Harbor & 75.8 & 76.1 & 82.6 & **85.6** \\ Swimming pool & 70.7 & 79 & 81.1 & **82** \\ Helicopter & 59.4 & 62.9 & 66.1 & **73.2** \\ \hline mAP (\%) & 74.8 & 76.2 & 78 & **81** \\ \hline \hline \end{tabular} Note: \(\surd\) indicates that the component is used, - indicates that the component is not used, and bold values indicate the performance of the best model in each scenario.  
\end{table}
Table 2: Ablation study results on the DOTA1.0 dataset.  
-spaced targets. Therefore, incorporating an additional rotation dimension is essential for improving the recognition accuracy of dense rotated images.