# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 4 Experiments
### Comparison of Results for Different Datasets  
#### 4.5.1 Evaluation on DOTA  
Table 7 shows the results of our proposed MIHE-Net and other remote sensing image rotation detection methods on the DOTA dataset. For the Basketball Court (BC), Soccer Ball Field (SBF), Roundabout (RA), Swimming Pool (SP), and Helicopter (HC) categories, MIHE-Net achieved the highest detection accuracy of 88.42%, 73.16%, 71.76%, 82.46%, and 84.76%, respectively. Additionally, MIHE-Net obtained the best average result of 81.72% across all categories. LSKNet-T [26] performed the best in remote sensing rotated object detection tasks, as it not only considers the extraction of rotational features in remote sensing targets but also selectively expands the spatial receptive field of large-sized targets to capture more scene context information by combining target features with contextual features to assist target detection. However, the use of large-kernel convolutions in LSKNet-T may introduce substantial background noise, which is unfavorable for the precise detection of small targets. Meanwhile, it ignores the impact of features extracted by the detector on subsequent rotational bounding box regression. MIHE-Net remedies this deficiency, increasing the mAP by 0.65%.  
We visualize some detection results of MIHE-Net on the DOTA dataset in Figure 4. The three images in the first row depict targets of various scales, for which MIHE-Net still achieves precise detection. The three images in the second row contain many small and dense targets; nonetheless, MIHE-Net still accurately detects objects such as cars and ships by utilizing contextual information. The first image in the third row contains targets arranged in a rotating and dense manner, while the second image shows rotating multi-directional targets and the third contains targets with both large and small aspect ratios. Despite this, the rotating bounding boxes generated by MIHE-Net remain well-aligned with the targets, ensuring that all target areas are included without covering excessive background areas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**With MCI** & **With HEA** & **With Midpoint Offset** & **mAP** \\ \hline \(\bigstar\) & \(\bigstar\) & \(\bigstar\) & \(89.63\pm 0.28\) \\ \(\bigvee\) & \(\bigvee\) & \(\bigstar\) & \(91.28\pm 0.25\) \\ \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & \(90.96\pm 0.19\) \\ \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & \(91.22\pm 0.28\) \\ \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & \(91.86\pm 0.22\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact of individual components on UCAS-AOD dataset.  
\begin{table}
\begin{tabular}{c|c c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Backbone** & **PL** & **BD** & **BR** & **GTF** & **SV** & **LV** & **SH** & **TC** & **BC** & **ST** & **SBF** & **RA** & **HA** & **SP** & **HC** & **mAP** \\ \hline DAL [59] & ResNet-101 & 88.68 & 76.55 & 45.08 & 66.00 & 67.00 & 76.76 & 79.74 & 90.84 & 79.54 & 78.45 & 57.71 & 62.27 & 69.05 & 73.14 & 60.11 & 71.44 \\ CFC-Net [40] & ResNet-101 & 89.08 &  
These results indicate that capturing multi-scale texture features when objects exhibit various scales and achieving bounding box regression when objects are rotated in multiple directions are both crucial for accurately detecting rotated targets in remote sensing images. Our MCI module design can capture local and global contextual information at different scales, combining target features with contextual features while maintaining the extraction of fine-grained detail features. In addition, the Midpoint Offset Loss Function makes the network's regression results for rotated bounding boxes more accurate.  
Figure 4: Example detection results of MIHE-Net on the DOTA dataset.