# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 5 Conclusions  
This paper introduces a novel two-stage rotating detection network called DDL R-CNN for detecting multi-directional image targets. The method consists of a dynamic direction learning module and a boundary center region offset generation network, which addresses the angle matching issues at the feature level and the training instability issues at the sample level present in existing methods, and has undergone extensive experimentation. The experimental results showed that the mAP was 90.82% in the UCAS-AOD dataset and 97.81% in the HRSC2016 dataset. Compared with the existing advanced two-stage rotational detection network, the proposed method significantly improves the detection accuracy of multi-directional targets and maintains a certain level of competitiveness in terms of efficiency compared with the single-stage rotational detection network.  
Future research is expected to be carried out in the following directions. Cross-domain applications could see DDL R-CNN applied to the medical image analysis field, such as in tumor detection or organ segmentation, where the adaptive nature of the network could significantly enhance detection accuracy due to the varied shapes and orientations of targets. Video object detection is another area where DDL R-CNN could be explored, particularly in dynamically changing scenes for detecting and tracking targets, with the integration of temporal information allowing the model to better handle target motion and changes. Additionally, small-object detection in specific scenarios like surveillance videos or drone-captured images remains a challenge, and research could focus on optimizing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Params(M)** & **FLOPs(G)** & **mAP** \\ \hline \multicolumn{4}{c}{One-stage} \\ \hline R3 Det & 41.9 & 336 & 77.89 \\ CFA [39] & 33.6 & 194 & 78.95 \\ SASM [40] & 36.6 & 194 & 78.78 \\ AO2-DETR [41] & 74.3 & 304 & 79.22 \\ S\({}^{2}\)ANet & 38.6 & 198 & 79.42 \\ R3 Det-GWD [21] & 41.9 & 336 & 80.23 \\ RTMDet-R [42] & 52.3 & 205 & 81.88 \\ R3 Det-KLD [43] & 41.9 & 336 & 83.92 \\ \hline \multicolumn{4}{c}{Two-stage} \\ \hline G.V. [10] & 41.1 & 198 & 75.77 \\ CenterMap-Net & 41.1 & 198 & 79.34 \\ CSL & 37.4 & 236 & 80.23 \\ O-RCNN [44] & 41.1 & 199 & 81.87 \\ KFIoU [45] & 58.8 & 206 & 84.93 \\ RVSA [46] & 114.4 & 414 & 86.74 \\ Rol Transformer & 55.1 & 200 & 88.95 \\ DDL R-CNN & 74.38 & 211.97 & 90.82 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Complexity analysis and comparison between DDL module and BC-ROPN for FLOPs and model parameters in UCAS-AOD dataset.  
DDL R-CNN to improve recognition capabilities for these small targets. Lastly, multimodal data fusion, combining DDL R-CNN with other data types such as LiDAR and infrared images, could be investigated to enhance the model's understanding of complex scenes. These directions not only pave the way for more accurate and robust detection systems but also expand the applicability of DDL R-CNN across various domains.  
Conceptualization, D.J.; software, W.S.; validation, W.S.; investigation, W.S.; methodology, W.S.; formal analysis, W.S. and D.J.; resources, D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.