## 4 Experimental Results
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Method** & **Recall \(\uparrow\)** & **F1 Score \(\uparrow\)** & **Error Rate \(\downarrow\)** & **MIoU \(\uparrow\)** \\ \hline \multirow{8}{*}{Day} & U-Net & 80.39\% & 85.99\% & 10.86\% & 77.99\% \\  & U-Net+CAM & 85.67\% (**+5.28\%**) & 88.85\% (**+2.86\%**) & 8.53\% (**-2.33\%**) & 81.92\% (**+3.93\%**) \\ \cline{2-6}  & Att-UNet & 87.23\% & 89.66\% & 8.17\% & 82.91\% \\  & Att-UNet+CAM & 89.80\% (**+2.57\%**) & 90.80\% (**+1.14\%**) & 7.16\% (**-1.01\%**) & 84.74\% (**+1.83\%**) \\ \cline{2-6}  & UNeXt & 89.78\% & 91.00\% & 7.31\% & 84.80\% \\  & UNeXt+CAM & 89.77\% (**-0.01\%**) & 91.23\% (**+0.23\%**) & 7.31\% (**-0.00\%**) & 85.08\% (**+0.28\%**) \\ \cline{2-6}  & Laplacian & 89.37\% & 90.67\% & 7.58\% & 84.50\% \\  & Laplacian+CAM & 89.90\% (**+0.53\%**) & 91.31\% (**+0.64\%**) & 7.07\% (**-0.51\%**) & 85.50\% (**+1.00\%**) \\ \hline \multirow{8}{*}{Night} & U-Net & 68.90\% & 76.24\% & 15.53\% & 65.03\% \\  & U-Net+CAM & 78.00\% (**+9.10\%**) & 82.13\% (**+5.89\%**) & 12.82\% (**-2.71\%**) & 71.96\% (**+6.93\%**) \\ \cline{2-6}  & Att-UNet & 78.74\% & 83.67\% & 12.65\% & 73.65\% \\  & Att-UNet+CAM & 83.37\% (**+4.63\%**) & 83.32\% (**-0.35\%**) & 13.44\% (**+0.79\%**) & 73.84\% (**+0.19\%**) \\ \cline{2-6}  & UNeXt & 81.75\% & 84.98\% & 12.04\% & 75.71\% \\  & UNeXt+CAM & 84.42\% (**+2.67\%**) & 87.09\% (**+2.11\%**) & 10.61\% (**-1.43\%**) & 78.31\% (**+2.60\%**) \\ \cline{2-6}  & Laplacian & 76.69\% & 83.39\% & 12.56\% & 73.62\% \\  & Laplacian+CAM & 78.04\% (**+1.35\%**) & 84.33\% (**+0.94\%**) & 12.55\% (**-0.01\%**) & 74.43\% (**+0.81\%**) \\ \hline \multirow{8}{*}{Day + Night} & U-Net & 79.18\% & 84.97\% & 11.35\% & 76.62\% \\  & U-Net+CAM & 84.86\% (**+5.68\%**) & 88.14\% (**+3.17\%**) & 8.98\% (**-2.37\%**) & 80.87\% (**+4.25\%**) \\ \cline{2-6}  & Att-UNet & 83.71\% & 87.91\% & 8.99\% & 80.60\% \\ \cline{1-1}  & Att-UNet+CAM & 87.33\% (**+3.62\%**) & 89.15\% (**+1.24\%**) & 8.50\% (**-0.49\%**) & 82.28\% (**+1.68\%**) \\ \cline{1-1} \cline{2-6}  & UNeXt & 88.93\% & 90.36\% & 7.81\% & 83.84\% \\ \cline{1-1}  & UNeXt+CAM & 89.20\% (**+0.27\%**) & 90.80\% (**+0.44\%**) & 7.66\% (**-0.15\%**) & 84.36\% (**+0.52\%**) \\ \cline{1-1} \cline{2-6}  & Laplacian & 88.04\% & 89.91\% & 8.10\% & 83.36\% \\ \cline{1-1}  & Laplacian+CAM & 88.65\% (**+0.61\%**) & 90.58\% (**+0.67\%**) & 7.65\% (**-0.45\%**) & 84.33\% (**+0.97\%**) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the performance of the four segmentation models and the corresponding models with CAM added. The values in parentheses indicate the changes after adding CAM. serving as the reference, achieves a recall of 89.35%, an F1 score of 89.81%, an error rate of 8.16%, and an MIoU of 83.11%. The incremental addition of each module demonstrates their individual and collective efficacy in addressing the limitations outlined in the introduction, the result of the ablation study aimed at evaluating the contribution of each component in the proposed model. The introduction of multi-scale input significantly enhances performance, achieving a recall of 89.62%, an F1 score of 91.40%, an error rate of 7.16%, and an MIoU of 85.51%. This improvement is attributed to the module's capability to process images at multiple resolutions, effectively capturing both local fine-grained details and broader contextual information. From the perspective of explainable AI (XAI), this multi-scale input mechanism enhances the model's interpretability by allowing it to consider features at various scales, making the decision-making process more transparent and understandable. This addresses a critical limitation of traditional CAM methods, which rely on single-scale features and consequently struggle to precisely localize cloud boundaries, especially in complex scenarios like thin cirrus or dense cumulonimbus clouds.