## 5 Experiments
### Ablation Experiments  
The ablation experiment results obtained based on the proposed FAS, DDH, and ECS methods are presented in Table 5. It can be observed from the table that, using Inception-ResNet101 as the baseline, the FAS module alone achieves a 78.6% mAP on the DOTA1.0 rotated-target detection task.  
Based on FAS, adding the DDH or ECS module individually results in an improvement in mAP. Adding the DDH module alone increases the mAP by 1.5%, indicating that the decoupled detection head contributes to enhancing target detection accuracy; however, the FPS decreases by 1.3 frames, suggesting that the addition of a feature extraction branch in the decoupled method with non-shared weights increases the overall computational overhead of the network.  
Adding the ECS module alone can increase mAP by 1.2% with almost no decrease in FPS. This indicates that the elliptical center sampling method improves model accuracy without increasing computational overhead, and that elliptical center sampling is superior to rectangular sampling in rotated-object detection tasks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Maximum Iterations & 1,600,000 (20 epochs, 80,000 per epoch) \\ Images per Epoch & 118,000 \\ Batch Size & 8 \\ GPU Count & 2 \\ Optimizer & SGD Momentum (momentum = 0.9, epsilon = \(1\times 10^{-5}\)) \\ Initial Learning Rate & 0.01 (\(5\times 10^{-4}\times 2\times 1.25\times 8\times 1\)) \\ Warmup Iterations & 20,000 (0.25 epochs) \\ Learning Rate Decay Steps & 960,000, 1,280,000, 1,600,000 \\ Learning Rate Decay Factor & 0.1 (0.01 \(\rightarrow\) 0.001 \(\rightarrow\) 0.0001) \\ RPN Classification Loss Weight & 1.0 \\ RPN Regression Loss Weight & 1.0 \\ RPN Balancing Factor \(\sigma\) & 3.0 \\ SHARE_HEADS & True \\ GRADIENT_CLIPIPING_BY_NORM & None \\ MUTILPY_BIAS_GRADIENT & None \\ FAS-FPN Layers & 7 \\ FAS-FPN Channels & 384 \\ FAS-FPN Activation & ReLU \\ Gradient Clipping & Not enabled \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training hyperparameters for FAS and final detector training  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Phase** & **Iteration Range** & **Description** & **Learning Rate** \\ \hline Warm-up & 0–20,000 & Warm-up phase & Linearly increases \\  & & & to 0.01 \\ Main Training & 20,000–960,000 & Initial learning rate & 0.01 \\ First Decay & 960,000–1,280,000 & First decay phase & \(\times\)0.1 (0.001) \\ Second Decay & 1,280,000–1,600,000 & Second decay phase & \(\times\)0.1 (0.0001) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Learning rate scheduling strategy.  
Simultaneously adding these two modules can increase mAP to 82.6%, while the FPS is 24.4 frames, a decrease of 1.4 frames compared to the baseline. This indicates that the decoupled detection head slightly increases the overall computational overhead of the model. However, the increase in mAP is more significant.  
The results of the fusion experiment indicate that the ECS and DDH module optimize model accuracy from two perspectives, model structure and sampling method, resulting in a significant improvement in detection accuracy. Although the addition of the DDH module results in a more substantial increase in model accuracy, it also introduces a corresponding increase in computational overhead.  
The three optimization methods proposed in this paper significantly improve upon the Inception-ResNet101 baseline model. Conversely, removing any of these modules individually leads to a decrease in model accuracy, thereby demonstrating the effectiveness of FAS, DDH, and ECS.