# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Comparison with Other New Technologies
Moreover, R2 CNN also performs poorly in identifying small targets, as illustrated in the fourth image of the first row, aligning with the challenges mentioned in the literature regarding small-target detection--small targets occupy fewer pixels in the image and have fewer distinct features, increasing the difficulty of detection. Although O-RCNN's detection results are similar to those of our method, the network still faces recognition challenges when the target color is similar to the background color, as indicated by the red dashed boxes in the first and third images of the first row. Specifically, the issues with O-RCNN mirror those of our method; when targets are close in color to the background, O-RCNN's detection performance significantly declines. This challenge highlights that even advanced object detection algorithms struggle to avoid misjudgments and omissions in complex background conditions. Furthermore, as shown in Table 5, our method also demonstrated excellent performance on the HRSC2016 dataset. Employing R-50-FPN as the backbone network, our method acquires an mAP of 97.81%, representing the highest value among all the methods being compared. Compared to methods such as Gliding Vertex, PIoU, and R3 Det, our method outperformed them in mAP by 9.61%, 8.61%, and 8.55%, respectively. The specific experimental results are shown in Figure 6, where the detection boxes tightly mark the direction and edges of the ships and show strong discrimination ability for similar objects, such as elongated piers, warehouses, water waves, and containers. Similar to the situation on the UCAS-AOD dataset, the results on this dataset indicate that existing methods with  
Figure 5: Comparison of detection results between DDL R-CNN and existing methods on UCAS-AOD dataset. rotating convolutional kernels, such as Rotated RPN, use multiple predefined and fixed rotation angles for feature extraction. In port scenarios, the orientation of ships is arbitrarily distributed at 0\({}^{\circ}\) to 360\({}^{\circ}\), and these predefined rotation angles are difficult to effectively align with the actual ships, leading to inaccurate directional feature information during feature extraction. Moreover, our method refines the rotation by incorporating the extracted directional features on the target's bounding rectangle, resulting in more compact rotation boxes compared to direct angle-based rotation methods like PIoU, and thus enabling the extraction of more accurate features. Further comparative visualization results on the HRSC2016 dataset are shown in Figure 7, where the detection outcomes of RetinaNet, R2 CNN, and O-RCNN are contrasted  
Figure 6: Visualization result of rotating anchor box generated by DDL R-CNN on HRSC2016 dataset. The figure displays the top 200 anchor boxes for each image. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Backbone** & **mAP** \\ \hline R2 CNN [11] & R-101 & 73.07 \\ RoI Transformer [15] & R-101-FPN & 86.20 \\ PIoU [34] & DLA-34 & 89.20 \\ Rotated RPN [9] & R-101 & 79.08 \\ DRN [18] & H-34 & - \\ R3 Det [19] & R-101-FPN & 89.26 \\ DCL [35] & R-101 & 89.46 \\ CSL [36] & R-50 & 89.62 \\ DAL [33] & R-101-FPN & 89.77 \\ Gliding Vertex [10] & R-101-FPN & 88.20 \\ S\({}^{2}\)ANet [37] & R-101-FPN & 90.17 \\ CenterMap-Net [38] & R-50-FPN & - \\ GWD [21] & R-101 & 89.85 \\ ReDet [16] & R-101 & 90.46 \\ DDL R-CNN & R-50-FPN & 97.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Examples of results compared with various methods on the HRSC2016 dataset using directional R-CNN with R-50-FPN backbone. with the method proposed in this study. Significant differences are observed in the aspects of small-target recognition, anchor box convergence, and the accuracy of rotation direction. Specifically, the method proposed in this study excels in the recognition of small targets. For instance, as demonstrated by the red dashed box in the first image of the first row, RetinaNet failed to detect a small target that was successfully identified by our method.