# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 4 Experiments
### Ablation Experiment  
We conducted relevant ablation experiments on the DOTA-v1.0, HRSC2016, and UCAS-AOD datasets to validate the performance of the proposed MIHE-Net. First, we conducted research on the multi-scale kernel design in the MCI module using the DOTA dataset. Table 1 shows that when only smaller 3 \(\times\) 3 kernels are used, the model achieves suboptimal performance. This can be attributed to its limited ability to extract texture information. To address this, a multi-scale kernel structure with kernel sizes ranging from 3 \(\times\) 3 to 11 \(\times\) 11 and a stride of 2 was implemented, under which the model achieved its highest performance. Subsequent experiments tested a configuration with increased kernel sizes and a stride of 4, but the performance fell short of the optimal level. Further investigations revealed that exclusive use of large kernels not only elevates computational complexity but also degrades performance, with mAP dropping by 0.49% and 0.84%, respectively. This suggests that large kernels may introduce background noise, leading to performance deterioration. On the basis of the multi-scale kernel design in Table 1 that achieved optimal performance, we studied the number of kernels in the multi-scale kernel design. As shown in Table 2, with the increment in the number of kernels, the network's performance exhibited a gradual upward trajectory, reaching the best effect when five kernels were used. Next, we verified the effectiveness of the HEA module. First, its impact was examined by applying HEA with different kernel sizes, with the results shown in Table 3. Empirical evidence suggests that smaller kernels struggle to model long-range dependencies, resulting in limited performance, whereas larger kernels mitigate this issue by integrating broader contextual information. Our proposed kernel size expansion strategy, which involves increasing the stripe convolution kernel size as the network blocks deepen, achieves optimal performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kernel Design** & **Parameters (MB)** & **FLOPs (G)** & **mAP** \\ \hline (3, 3, 3, 3, 3) & 12.62 & 62.40 & 79.94 \(\pm\) 0.12 \\ (3, 5, 7, 9, 11) & 13.69 & 70.20 & 81.72 \(\pm\) 0.15 \\ (3, 5, 9, 13, 17) & 14.99 & 79.57 & 81.06 \(\pm\) 0.12 \\ (11, 11, 11, 11, 11) & 15.13 & 80.61 & 80.45 \(\pm\) 0.10 \\ (15, 15, 15, 15, 15) & 17.44 & 92.45 & 80.32 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multi-scale kernel design. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kernel Number** & **Parameters (MB)** & **FLOPs (G)** & **mAP** \\ \hline
2 & 12.56 & 61.95 & 78.64 \(\pm\) 0.23 \\
3 & 12.78 & 63.57 & 79.16 \(\pm\) 0.18 \\
4 & 13.13 & 66.24 & 80.07 \(\pm\) 0.12 \\
5 & 13.69 & 70.20 & 81.72 \(\pm\) 0.15 \\
6 & 14.35 & 75.26 & 81.60 \(\pm\) 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Kernel number. Because our MIHE-Net is composed of four stages, we subsequently investigated the deployment location of the HEA module. Table 4 shows that deploying the HEA module at any stage can provide performance improvements. Table 5 presents the outcomes of the baseline model incorporating our proposed MCI module, HEA module, and Midpoint Offset Loss Function on the HRSC2016 dataset. On this dataset, the baseline model yields an mAP of 89.21%, which is because traditional detection networks with fixed-scale designs struggle to simultaneously and accurately detect objects of various sizes. When combined with our proposed MCI module, the detector's performance is optimized by 2.31%. This indicates that the MCI module can perform refined scale modeling in rotated object detection tasks, fully extracting local features of objects at different scales while incorporating feature-rich contextual information to improve the network's detection performance. Adding HEA module to the network enables the detector to extract global contextual information.