# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 4 Experiment
### Comparative Experiment
#### 4.5.2 Experiment on HRSC-2016  
We compared this method with other methods on the HRC-2016 dataset. From Table 6, it can be seen that compared to other methods, our method achieves 1.11% and 0.81% higher AP values than \(R^{3}\)Det and AR2 Det and is superior to the other one-stage and two-stage detectors in the table. For targets with high aspect ratios and significant scale differences in aerial targets, the detection performance of this method is better.  
Figure 6 shows the detection results of our method on the HRSC-2016 dataset. The first row of images contains a large number of targets with significant scale differences. Our method can accurately detect ships of various scales. The ship in the second row of images has the characteristics of a high aspect ratio and a dense multi-directional layout, and the detection box can better fit the target. This indicates that DCM optimizes the convolution feature extraction method and is more suitable for multi-directional aerial targets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**PL**} & \multicolumn{1}{c}{**BD**} & \multicolumn{1}{c}{**BR**} & \multicolumn{1}{c}{**GTF**} & \multicolumn{1}{c}{**SV**} & \multicolumn{1}{c}{**LV**} & \multicolumn{1}{c}{**SH**} & \multicolumn{1}{c}{**TC**} & \multicolumn{1}{c}{**BC**} & \multicolumn{1}{c}{**ST**} & \multicolumn{1}{c}{**SBF**} & \multicolumn{1}{c}{**RA**} & \multicolumn{1}{c}{**HA**} & \multicolumn{1}{c}{**SP**} & \multicolumn{1}{c}{**HC**} & \multicolumn{1}{c}{**mAP**} \\ \hline \multicolumn{16}{c}{Two-stage:} \\ \hline ICN [12] & 81.36 & 74.30 & 47.70 & 70.32 & 64.89 & 67.82 & 69.98 & 90.76 & 79.06 & 78.20 & 53.64 & 62.90 & 67.02 & 64.17 & 50.23 & 68.16 \\ RRPN [34] & 88.52 & 71.20 & 31.66 & 59.30 & 51.85 & 56.19 & 57.25 & 90.81 & 72.84 & 67.38 & 56.69 & 52.84 & 53.08 & 51.94 & 53.58 & 61.01 \\ SCRDet [1] & 89.98 & 80.65 & 52.09 & 68.36 & 68.36 & 60.32 & 72.41 & 90.52 & 87.94 & 86.86 & 65.02 & 66.68 & 66.25 & 68.24 & 65.21 & 72.61 \\ \(A^{2}\)RMNet [35] & 89.84 & 83.39 & 60.06 & 73.46 & 79.25 & 73.07 & 87.88 & 90.90 & 87.02 & 87.35 & 60.74 & 69.05 & 79.88 & 79.74 & 65.17 & 78.45 \\ FAOD [36] & 90.21 & 79.58 & 45.49 & 76.41 & 73.18 & 68.27 & 79.56 & 90.83 & 83.40 & 84.68 & 53.40 & 65.42 & 74.17 & 69.69 & 64.86 & 73.28 \\ FR-Est [37] & 89.63 & 81.17 & 50.44 & 70.19 & 73.52 & 77.98 & 86.44 & 90.82 & 84.13 & 83.56 & 60.64 & 66.59 & 70.59 & 66.72 & 60.55 & 74.20 \\ CenterMap [38] & 88.88 & 81.24 & 53.15 & 60.65 & 78.62 & 66.55 & 78.10 & 88.83 & 77.80 & 83.61 & 49.36 & 66.19 & 72.10 & 72.36 & 58.70 & 71.74 \\ \hline
**Ours** & **90.23** & **86.71** & **61.24** & **84.65** & 70.09 & 80.54 & 88.01 & 90.45 & 87.46 & 76.89 & **69.89** & **81.37** & 78.91 & 68.89 & **71.48** & **79.38** \\ \hline \multicolumn{16}{c}{Each abbreviation is represented as:} \\ \hline \hline \multicolumn{16}{c}{\begin{tabular}{c} Full Name \\ \end{tabular} } & \begin{tabular}{c} plane \\ diamond \\ \end{tabular} & bridge & ground & small & large & vehicle & ship & tennis & basket- & storage & \begin{tabular}{c} second- \\ built \\ \end{tabular} & \
\begin{tabular}{c} round- \\ fault \\ \end{tabular} & harbor &
\begin{tabular}{c} swim- \\ map \\ pool \\ \end{tabular} & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._