## 3 Experiments and Results
### Experimental Results
#### 3.3.2 Distillation Experiment  
In practical applications, agricultural equipment is usually restricted by limited computational resources and strict parameter budgets. To address this issue, we not only compared the proposed deep segmentation model with mainstream methods but also evaluated the performance of the lightweight model and the effect after knowledge distillation. The results are shown in Table 3.  
On the publicly available GLH-water dataset, DeepLabv3+ achieved an IoU of 79.80% with 5.81 million parameters and 211.47 GFLOPs, while PSPNet reached an IoU of 75.19% with 2.38 million parameters and 24.12 GFLOPs. In contrast, our lightweight model  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Params (M)** & **FLOPs (G)** & **IoU (\%)** \\ \hline MobileNetV2 [45] & 6.644 & 136.064 & 86.19 \\ MobileNetV2+LKF & 6.644 & 136.088 & 88.86 \\ CADCN+LKF & 0.10 & 72.908 & 81.62 \\ LKF-DCANet & 0.22 & 71.377 & 82.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of module combinations: parameters, FLOPs, and IoU on GLH-water.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Params (M)** & **FLOPs (G)** & **IoU (\%)** \\ \hline DeepLab-V3+ [21] & 5.81 & 211.47 & 79.8 \\ PSP-Net [18] & 2.38 & 24.12 & 75.19 \\ MobileNetV2+LKF & 6.644 & 136.088 & 88.86 \\ LKF-DCANet & 0.22 & 71.377 & 82.55 \\ Distilled LKF-DCANet & 0.22 & 71.377 & 85.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison between lightweight and distilled models on GLH-water dataset.  
achieved a higher IoU of 82.55% using only 0.22 million parameters and 71.38 GFLOPs, demonstrating its suitability for deployment on resource-constrained platforms.  
Furthermore, after applying feature-based knowledge distillation, the model retained the same architecture and computational cost, but the IoU improved to 85.95%--a relative increase of 3.4%. These results indicate that transferring discriminative feature representations from the deep network significantly enhances the performance of the lightweight model.  
As illustrated in Figure 10, the red boxes highlight the qualitative differences in segmentation results after integrating various modules. The baseline model (MobileNetV2) struggles in regions with natural illumination variation, water quality differences, and vegetation occlusion, leading to noticeable misclassifications and blurred boundaries. After incorporating the LKF module, the model demonstrates enhanced noise resilience. The red-boxed regions show clearer and more continuous water boundaries, with a significant reduction in false positives, indicating that the LKF module effectively stabilizes feature representations and suppresses optical noise.  
When compressing the model using the shallow CADCN encoder, segmentation accuracy degrades, particularly in narrow and fragmented water bodies. The red boxes reveal broken edges and increased prediction noise, reflecting reduced representational capacity under aggressive model simplification. By applying a feature-based knowledge distillation strategy (Distilled LKF-DCANet), the visual results show more coherent contours and fewer misclassified pixels in complex backgrounds.