## 4 Experiments
### Visualization  
To better understand the superiority of our method, we visualize the predicted oriented bounding boxes, corresponding scores, and heatmaps. We compare the results of our WDARFNet with the baseline on the DOTA V1.0 and DIOR-R test datasets. The experiment uses Oriented R-CNN [13] as the detector and compares the performance of WDARFNet and ResNet50 as backbone networks. Figures 9 and 10, respectively, display the visual comparisons of our method and the baseline on the DOTA V1.0 and DIOR-R test datasets. From the visual results, it is evident that WDARFNet outperforms ResNet50 in several object detection tasks. Specifically, in the roundabout detection task, WDARFNet is able to more accurately draw the rotated bounding boxes of target objects, with superior boundary precision and coverage compared to ResNet50. In the basketball and tennis court detection tasks, the detection results of WDARFNet comprehensively and clearly cover the target areas, avoiding false negatives and duplicate bounding boxes. For complex scenes involving objects such as airplanes and ships, WDARFNet achieves clearer boundary delineation, avoiding overlap and false positives between targets, particularly in scenarios with tightly arranged multiple targets or when detecting targets obscured by shadows. Furthermore, in large-object detection tasks, such as in harbors, WDARFNet's bounding box coverage is more comprehensive, demonstrating robustness to complex shapes and backgrounds. \begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **mAP** \\ \hline WDARFNet (A+H) & 64.49 \\ WDARFNet (A+V) & 64.28 \\ WDARFNet (A+D) & 62.92 \\ WDARFNet (A+H+V) & 65.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on the effectiveness of different high-frequency component combinations in WDARFNet. The DIOR-R dataset was utilized in this experiment. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **Backbone** & **PL** & **BD** & **BR** & **GTF** & **SV** & **LV** & **SH** & **TC** & **BC** & **ST** & **SBF** & **RA** & **HA** & **SP** & **HC** & **mAP** \\ \hline Two-stage: & & & & & & & & & & & & & & & & & & & & & \\ O-Faster R-CNN [1] & ResNet101 & 79.09 & 69.12 & 17.17 & 63.49 & 34.20 & 37.16 & 36.20 & 89.19 & 69.60 & 58.96 & 49.40 & 52.52 & 46.69 & 44.80 & 46.30 & 52.93 \\ RoI-Transformer [9] & ResNet101 & 88.64 & 78.52 & 43.44 & 75.92 & 68.81 & 73.68 & 83.59 & 90.74 & 77.27 & 81.46 & 58.39 & 53.54 & 62.83 & 58.93 & 47.67 & 69.56 \\ Centralde [53] & ResNet109 & 89.02 & 80.56 & 94.41 & 61.98 & 77.99 & 74.19 & 83.74 & 89.44 & 78.01 & 83.52 & 47.64 & 65.93 & 63.68 & 67.07 & 61.59 & 71.59 \\ SRCDT [19] & ResNet101 & 89.98 & 80.65 & 52.09 & 68.36 & 68.36 & 60.32 & 72.41 & 90.85 & 87.94 & 86.86 & 65.02 & 66.68 & 66.25 & 68.24 & 65.21 & 72.61 \\ Gliding Vertex * [14] & ResNet101 & 89.64 & 85.00 & 52.26 & 77.34 & 73.01 & 73.14 & 86.82 & 90.74 & 79.02 & 68.81 & 59.55 & 70.91 & 72.94 & 70.86 & 57.32 & 75.02 \\ RoIPC [12] & ResNet102 & ResNet108 & 88.79 & 82.64 & 53.97 & 74.00 & 78.13 & 84.06 & 88.04 & 90.87 & 87.75 & 61.67 & 60.39 & 75.96 & 68.07 & 63.59 & 76.25 \\ CSL* [10] & ResNet152 & 90.25 & 85.53 & 54.64 & 75.31 & 70.44 & 73.51 & 77.62  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**O-**} & \multicolumn{3}{c}{**Roll-**} & \multicolumn{3}{c}{**RCNN**} & \multicolumn{3}{c}{**O-FCOS**} & \multicolumn{3}{c}{**S\({}^{2}\)A-Net**} & \multicolumn{3}{c}{**R\({}^{3}\)Det**} & \multicolumn{3}{c}{**Vertex**} & \multicolumn{3}{c}{**KIFoU**} & \multicolumn{3}{c}{**SASA**} & \multicolumn{3}{c}{**GWD**} & \multicolumn{3}{c}{**KLD**} & \multicolumn{3}{c}{**O-Faster**} & \multicolumn{3}{c}{**O-ATSS**} & \multicolumn{3}{c}{**CFA [22]**} & \multicolumn{3}{c}{**Toppoints**} & \multicolumn{3}{c}{**Trans.