# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Ablation Experiment
#### 4.4.1 Evaluation of Different Approaches  
Table 1 presents the results from the UCAS-AOD dataset. The baseline model achieved an mAP of 89.62% without any additional modules. This is because the conventional R-CNN [27] convolutional structure only performs fixed multi-angle rotations on the convolutional kernels, which struggle to effectively model targets with diverse orientations across different categories. When the DDL module was introduced alone, the performance increased to 89.96%. This indicates that extracting rough directional features from the targets in the images and generating various rotation angles and weights for the convolutional kernels provide more accurate spatial directional information for subsequent feature extraction. Furthermore, when BC-ROPN was integrated into the model alone, the mAP reached 90.14%. This suggests that the rotating anchor box mechanism used by the network allows for clearer and more efficient rotation directions, avoiding the blurring of extracted directional features, which could lead to excessively high loss function values. Lastly, when both the DDL module and BC-ROPN were used in conjunction, the model's mAP reached 90.82%, which was the best performance among all experimental configurations. This result confirms the synergistic effect of the two modules, and their combined action helps enhance the model's ability to extract spatial directional features of rotating targets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DDL** & **BC-ROPN** & **Params(M)** & **FLOPs(G)** & **mAP** \\ \hline \(\bigtimes\) & \(\bigtimes\) & 41.14 & 211.43 & 89.62 \\ \(\bigvee\) & \(\bigtimes\) & 65.87 & 211.85 & 89.96 \\ \(\bigtimes\) & \(\bigvee\) & 40.74 & 211.41 & 90.14 \\ \(\bigvee\) & \(\bigvee\) & 74.38 & 211.97 & 90.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental study of replacement strategy ablation for UCAS-AOD dataset on the DDL module and BC-ROPN. The experiments were conducted on R-CNN of the ResNet-50 backbone network with the DDL module and BC-ROPN. Here, “\(\bigtimes\)” means that the method was not used and “\(\bigvee\)” means that the method was used. Additionally, Table 1 also includes information regarding the detection speed (efficiency) and the number of parameters. The baseline model has 41.14 M parameters and achieves 211.43 G FLOPs. When the DDL module is introduced, the Params rise to 65.87 M, and the FLOPs also increase to 211.85 G compared to the baseline model. This indicates that the DDL module, which extracts spatial directional features in advance and generates rotation angles and angle weights based on these features, does increase the model's Params to some extent, but it does not significantly raise the computational complexity. When BC-ROPN is introduced alone, the Params decrease to 40.74M and the FLOPs drop to 211.41 G. This is because BC-ROPN employs a lightweight fully convolutional network to integrate different features and uses a new rotation box regression mechanism instead of the original angle regression, resulting in a reduction in Params and a slight decrease in FLOPs compared to the baseline model. After introducing both the DDL module and BC-ROPN, the Params and FLOPs increased to 74.38 M and 211.97 G, respectively. However, this variation is within an acceptable range, once again indicating the synergistic effect of the DDL module and BC-ROPN, where the advanced extraction of spatial directional features by the DDL module is applied to feature fusion in BC-ROPN and ultimately used for weighted fine-tuning of the rotation boxes. This approach not only improves detection accuracy but also maintains high computational efficiency. On the HRSC2016 dataset, we observed similar trends. The combined use of different modules achieved better performance compared to using a single module alone.