# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Comparison with Other New Technologies  
In this experimental setup, we benchmarked our proposed DDL R-CNN against 8 target detection networks on the UCAS-AOD dataset and 14 methodologies on the HRSC2016 dataset. Table 4 reports the detailed comparison results for UCAS-AOD. On the UCAS-AOD dataset, our method achieved precision rates of 90.73% for vehicles and 90.91% for aircraft, with an overall mAP of 90.82%. Compared to advanced methods such as RetinaNet, RoI Trans, RIDet-Q, and CFC-Net, our method outperformed them in average precision by 4.32%, 1.87%, 1.59%, and 1.33%, respectively. The experimental results are shown in Figure 4, demonstrating our method's effective detection of both aircraft and cars. The three images on the left half show detection boxes closely fitting the edges of \begin{table}
\begin{tabular}{c c c c c} \hline
**Backbone** & **n** & **Params(M)** & **FLOPs(G)** & **mAP** \\ \hline R50 [25] & 1 & 41.14 & 211.43 & 75.81 \\ R101 [25] & 1 & 60.13 & 289.33 & 76.11 \\ DDL-R50 & 1 & 41.18 & 211.85 & 76.97 \\ DDL-R50 & 2 & 52.25 & 211.89 & 77.17 \\ DDL-R50 & 4 & 74.38 & 211.97 & 77.35 \\ DDL-R50 & 6 & 96.52 & 212.06 & 77.38 \\ DDL-R50 & 8 & 118.72 & 212.27 & 77.38 \\ DDL-R50 & 10 & 144.53 & 212.68 & 77.38 \\ \hline \end{tabular}
\end{table} 
Table 2: Ablation experimental study of the number of kernels \(n\). The experiments were conducted using DDL R-CNN on the UCAS-AOD dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & \(R_{300}\) & \(R_{1000}\) & \(R_{2000}\) \\ \hline BC-ROPN (Recall) & 82.80\% & 91.60\% & 92.20\% \\ \hline \end{tabular}
\end{table} 
Table 3: Recall results for the UCAS-AOD dataset. aircraft of various sizes and orientations, including densely packed or interlaced areas where overlapping regions are distinguished by the detection boxes. The three images on the right half also show similar results, with densely distributed and multi-directional cars being represented by compact detection boxes. This outcome validates the efficacy of our approach in tackling complex detection tasks that encompass multi-directional, densely packed, or small targets; shape-like distractors; and occlusions due to light or shadows. The comparative visualization results on the UCAS-AOD dataset are depicted in Figure 5, illustrating the detection outcomes of RetinaNet, R2 CNN, and O-RCNN in contrast with the method proposed in this study. These methods exhibit significant differences in terms of anchor box size convergence and angular accuracy. Specifically, RetinaNet shows misidentification when the target color is similar to the background, as observable in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Airplane** & **Car** & **mAP** \\ \hline RetinaNet [28] & 87.62 & 85.37 & 86.50 \\ RIDet-Q [29] & 89.96 & 88.50 & 89.23 \\ SLA [30] & 90.30 & 88.57 & 89.44 \\ RoI Trans [15] & 89.90 & 87.99 & 88.95 \\ TIOE-Det [31] & 90.15 & 88.83 & 89.49 \\ CFC-Net [32] & 88.69 & 89.29 & 89.49 \\ RIDet-O [29] & 90.35 & 88.88 & 89.62 \\ DAL [33] & 90.49 & 89.25 & 89.87 \\ DDL R-CNN & 90.91 & 90.73 & 90.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with various methods on UCAS-AOD dataset. Figure 4: Visualization of the rotating anchor box generated by DDL R-CNN on the UCAS-AOD dataset. The figure displays the top 200 anchor boxes for each image. first and third images of the first row. Additionally, RetinaNet still misses the detection of small targets, as shown in the second and fourth images of the first row. This indicates its limitations in detecting small-sized targets against complex backgrounds. The detection results of R2 CNN reveal the network's performance constraints under specific conditions. When the target color is similar to the background color, R2 CNN's detection effect is also suboptimal. As seen in the first and second images of the first row, the network fails to accurately distinguish targets that are close in color to the background, demonstrating that R2 CNN's recognition ability is compromised in low-color-contrast situations.