## 4 Experiments
### Ablation Studies  
In this section, we conduct a series of ablation studies to analyze how different design choices influence the performance of WDARFNet. We use Oriented RCNN [13] as the detector to conduct a series of ablation studies on the DIOR-R dataset to investigate the effectiveness of WDARFNet. We initially demonstrate that the WDSSM and WDDSM modules, which integrate convolutional neural networks with Discrete Wavelet Transforms, significantly outperform pure convolutional methods. We further show that the WDSSM and WDDSM modules enhance the network's noise robustness. Finally, we examine the enhancement of the network through the Selective Kernel mechanism. **Kernel size (\(K\)) and dilation (\(D\)). ** Kernel size (\(K\)) and dilation (\(D\)) are critical factors that control the size of the receptive field (RF). To investigate their effects, we employed a dual-branch structure based on the settings of SKNet, and we present the results of the ablation study in Table 1. Table 1 demonstrates that the mAP (mean average precision) rises as the RF increases, and it indicates that increasing dilation (\(D\)) proves more effective at enhancing RF under similar overall complexity constraints, furthermore. This enhancement in network performance is more pronounced under conditions of relatively low overall complexity. Ablation study results indicate that the optimal parameter settings are (kernel size = 5, dilation = 1) and (kernel size = 7, dilation = 3), indicating that using a large-size kernel and higher dilation (\(D\)) offers slight advantages in both performance and complexity, and that an excessively small receptive field will hinder the performance of WDARFNet. **The Effects of Wavelet Domain Channel Selection Module and Wavelet Domain Spatial Selection Module. ** We conducted experiments to assess the effectiveness of WDDSM and WDSSM in the Wavelet Domain Selective Kernel module, with the corresponding ablation study results depicted in Table 2. We validated the effectiveness of WDDSM and WDSSM by replacing the backbone networks; Experiment 1, as depicted in Table 2, utilizes ResNet50 as its backbone, whereas Experiments 2 and 3 employ solely WDDSM or WDSSM, respectively, and Experiment 4 implements the complete WDARFNet as its \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kernel Design (K, D)** & **RF** & **GFLOPs** & **mAP** \\ \hline (3, 1), (5, 1) & 7 & 267.7 & 64.80 \\ (3, 1), (5, 3) & 15 & 267.7 & 65.11 \\ (5, 1), (7, 1) & 11 & 268.3 & 65.02 \\ (5, 1), (7, 3) & 23 & 268.3 & 65.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An ablation study of WDARFNet was conducted, exploring various combinations of kernel sizes (K) and dilation rates (D). The dataset DIOR-R was used in the experiment. backbone. The results of the ablation experiments reveal that both WDCSM and WDSSM improve network performance compared to the baseline ResNet50. Compared with the proposed WDCSM, the performance of the proposed WDSSM is more improved. This is attributed to WDSSM's effective assignment of weights to the features processed by large kernels in each branch and its spatial integration of these features, thereby allowing the network to dynamically adjust the receptive field for each object in space according to specific requirements. Furthermore, the combined use of WDCSM and WDSSM yields a marginal improvement in network performance over employing WDSSM alone. **Noise-robustness. ** Convolutional neural networks are generally susceptible to noise interference. As feature maps propagate through deeper layers of the network, the noise is amplified successively, resulting in incorrect final predictions. To address the aforementioned issue, we propose a Wavelet Domain denoising strategy to enhance the network's noise robustness. Subsequently, ablation experiments were conducted to verify the effectiveness of WDARFNet. In the training stage, ResNet50 and WDARFNet were used as backbone networks to train models on the clean DIOR-R training set for 12 epochs, respectively.