# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 4 Experiment
### Comparative Experiment  
#### 4.5.1 Experiment on DOTA  
Our method was compared with other recent methods on DOTA. As shown in Table 5, for some categories such as plane (PL), baseball diamond (BD), bridge (BR), ground track field (GTF), soccer ball field (SBF), roundabout (RA), and helicopter (HC), our detection results were the best, with AP values of 90.23%, 86.71%, 61.24%, 84.65%, 69.89%, 81.37%, and 71.48% respectively. By synthesizing 15 categories, the mAP of this paper reached 79.6%, which is the highest among all methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Settings** & **mAP (\%)** \\ \hline smooth-L1 & 84.35 \\ \(a=0.1\) & 87.85 \\ \(a=0.2\) & **90.41** \\ \(a=0.3\) & 88.64 \\ \(a=0.4\) & 88.59 \\ \(a=0.5\) & 85.37 \\ \(a=0.8\) & 84.02 \\ \(a=1.5\) & 79.82 \\ \hline \end{tabular}
\end{table}
Table 4: Impact of parameter a on model performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **PL** & **BD** & **BR** & **GTF** & **SV** & **LV** & **SH** & **TC** & **BC** & **ST** & **SBF** & **RA** & **HA** & **SP** & **HC** & **mAP** \\ \hline Single-stage: & & & & & & & & & & & & & & & & \\ \hline DRN [4] & 88.91 & 80.22 & 43.52 & 63.35 & 73.48 & 70.69 & 84.94 & 90.14 & 83.85 & 84.11 & 50.12 & 58.41 & 67.62 & 68.60 & 52.50 & 70.70 \\ R3 Det [29] & 88.76 & 83.09 & 50.91 & 67.27 & 76.23 & 80.39 & 86.72 & 90.78 & 84.68 & 83.24 & 61.98 & 61.35 & 66.91 & 70.63 & 53.94 & 73.79 \\ FFA3 [30] & 88.80 & 74.40 & 48.90 & 57.90 & 63.60 & 75.90 & 79.60 & 90.80 & 80.30 & 82.90 & 54.30 & 60.00 & 66.90 & 66.80 & 42.50 & 68.90 \\ GGHL [31] & 89.74 & 85.63 & 44.50 & 77.48 & 76.72 & 80.45 & 86.16 & 90.83 & 88.18 & 86.25 & 67.07 & 69.40 & 73.38 & 68.45 & 70.15 & 76.95 \\ DAL [6] & 88.68 & 76.55 & 45.08 & 66.80 & 67.00 & 76.76 & 79.74 & 90.84 & 79.54 & 78.45 & 57.71 & 62.27 & 69.05 & 73.14 & 60.11 & 71.44 \\ RIDe+O [32] & 88.94 & 78.45 & 46.87 & 72.63 & 77.63 & 80.68 & 88.18 & 90.55 & 81.33 & 83.61 & 64.85 & 63.72 & 73.09 & 73.13 & 56.87 & 74.70 \\ CADNet [33] & 87.80 & 82.40 & 49.40 & 73.50 & 71.10 & 63.50 & 76.60 & 90.9 & 79.20 & 73.30 & 48.40 & 60.90 & 62.00 & 67.00 & 62.20 & 69.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the mAP of different methods in the DOTA dataset and the AP of each category.  
This method aligns the rotation convolution with the target, enabling more accurate detection of targets with rotation angles. These results indicate that the key to achieving precise detection of aerial targets is to construct an adaptive feature extractor for rotating targets and design feature filters that can characterize the direction of the target. At the same time, in the training set stage of the network, the model training efficiency is improved by optimizing the gradient imbalance problem. These two modules work together to ensure maximum improvement in network performance.  
Figure 5 shows the detection performance of this method on the DOAT dataset, with the first row of images containing a large number of small targets of different categories and densely arranged. Our method can clearly detect every target, which proves that the dynamic rotation convolution proposed has good adaptability to direction, making the extracted features closely fit small targets, thereby improving the network's detection ability for small targets. The ships, docks, and other targets in the second row of images are distributed in multiple directions, and this method can also accurately detect each target direction. This suggests that the DCM module employed in this study enhances the network's ability to model target directional features, enabling the accurate detection of multiple targets with distinct directional disparities.