## 3 Results
### Ablation Experiments  
We also conducted ablation studies by systematically adding various modules to the baseline model, e.g., the RepLKNet network module, CBAM attention mechanism module, DynamicHead, WIoU loss function, and DynamicATSS sample allocation strategy, to assess their impacts on the overall performance of YOLOv11-RCDWD. To maintain the validity of comparisons, all models were trained on a consistent dataset under standardized parameter settings. The comprehensive results of the ablation studies are detailed in Table 4.  
Introducing the RepLKNet module significantly improved the model's detection accuracy. When used alone, RepLKNet increased the precision from 87.7% to 89.1%, recall from 78.0% to 79.9%, [EMAIL_ADDRESS] from 85.3% to 87.1%, and [EMAIL_ADDRESS]-0.95 from 63.5% to 66.7%. However, the detection speed increased from 1.3 ms to 3.2 ms, and GFLOPs \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **RepLK-** \\ **Net** \\ \end{tabular} & \begin{tabular}{c} **CBAM** \\ **idHead** \\ \end{tabular} & \begin{tabular}{c} **Dynam-** \\ **idHead** \\ \end{tabular} & \begin{tabular}{c} **WIoU** \\ **icATSS** \\ \end{tabular} & \begin{tabular}{c} **Dynam-** \\ **sion\%** \\ \end{tabular} & \begin{tabular}{c} **Pred-** \\ **sion\%** \\ \end{tabular} & \begin{tabular}{c} **Recall\%** \\ **Score/\%** \\ \end{tabular} & \begin{tabular}{c} **F1** \\ **Score/\%** \\ \end{tabular} & \begin{tabular}{c} **[EMAIL_ADDRESS]** \\ **0.5\%** \\ \end{tabular} & \begin{tabular}{c} **D Detection** \\ **-0.95\%** \\ \end{tabular} & \
\begin{tabular}{c} **Parameter-** \\ **Speed/ms** \\ \end{tabular} &
\begin{tabular}{c} **GFLOPs** \\ **test/M** \\ \end{tabular} \\ \hline - & - & - & - & - & 87.7 & 78.0 & 82.6 & 85.3 & 63.5 & 1.3 & 10.52 & 21.2 & 19.2 \\ - & - & - & - & 89.1 & 79.9 & 84.2 & 87.1 & 66.7 & 3.2 & 9.85 & 25.2 & 20.2 \\ - & - & - & - & 90.5 & 79.4 & 84.1 & 86.2 & 64.6 & 1.6 & 8.49 & 20.6 & 17.3 \\ - & - & - & - & 87.8 & 82.1 & 83.8 & 85.5 & 65.0 & 6.5 & 9.72 & 21.2 & 19.8 \\ - & - & - & \(\surd\) & - & 86.6 & 81.0 & 83.7 & 86.7 & 64.4 & 1.2 & 10.2 & 21.3 & 19.2 \\ - & - & - & \(\surd\) & 86.3 & 80.1 & 83.1 & 85.6 & 63.5 & 3.8 & 10.3 & 21.3 & 19.2 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 92.6 & 85.4 & 88.9 & 90.2 & 72.5 & 1.6 & 9.41 & 19.3 & 16.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation Experiment Results. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Model** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Precision/\%** \\ \end{tabular} & \begin{tabular}{c} **Recall/\%** \\ \end{tabular} & \begin{tabular}{c} **F1 Score/\%** \\ \end{tabular} & \begin{tabular}{c} **[EMAIL_ADDRESS]-** \\ **0.95\%** \\ \end{tabular} & \begin{tabular}{c} **[EMAIL_ADDRESS]-** \\ **Speed/ms** \\ \end{tabular} & \
\begin{tabular}{c} **Detection** \\ **Parameter/M** \\ \end{tabular} &
\begin{tabular}{c} **GFLOPs** \\ **MB** \\ \end{tabular} \\ \hline YOLOv11s- & 87.7 & 78.0 & 82.6 & 85.3 & 63.5 & 1.3 & 10.52 & 21.2 & 19.2 \\ YOLOv11s- & 90.5 & 79.4 & 84.1 & 86.2 & 64.6 & 1.6 & 8.49 & 20.6 & 17.3 \\ YOLOv11s- & 87.7 & 78 & 82.6 & 85.3 & 63.5 & 4.2 & 9.41 & 21.3 & 19.2 \\ YOLOv11s- & 86.4 & 81.1 & 83.7 & 86.9 & 64.6 & 3.3 & 9.08 & 21.7 & 18.5 \\ YOLOv11s- & 88.7 & 76.6 & 82.2 & 84.1 & 62.4 & 4.6 & 11.91 & 23.3 & 24.2 \\ YOLOv11s- & 84.8 & 81.4 & 83.1 & 86.6 & 64.5 & 4.1 & 8.36 & 20.2 & 17.2 \\ YOLOv11s- & 85.5 & 78.8 & 82.0 & 86 & 63.7 & 3.7 & 8.42 & 20.5 & 17.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results Using Different Attention Mechanisms. increased from 21.2 M to 25.2 M, indicating that while RepLKNet enhanced performance, it also significantly increased the computational burden. In contrast, the CBAM attention mechanism module improved the precision to 90.5%, while significantly reducing the number of parameters (from 10.52 M to 8.49 M) and memory overhead (from 19.2 MB to 17.3 MB). Moreover, the detection speed slightly increased to 1.6 ms, demonstrating a good balance between accuracy and efficiency.