## 4 Experiments and Results
### Comparative Experiment and Results  
This study selected six typical and widely used networks (U-Net [27], HRNet [28], DeeplabV3+ [44], SegNet [45], SegFormer [46], PSPNet [47]) as the control group for comparison with the ShadeNet proposed in this study. The histograms of mIOU results for each model are shown in Figure 12.  
As shown in Table 3, Figures 12 and 13, the ShadeNet demonstrates exceptional segmentation performance in complex scenarios. Particularly when handling roads, dark-colored water bodies, dark-colored vegetation, black roofs, shadow areas, and other objects similar to shade houses, its accuracy significantly outperforms the other models in the comparison. Specifically, in the scenario shown in Figure 13a, the ShadeNet, along with Segformer [46], demonstrates outstanding performance in distinguishing roads and shade houses, significantly outperforming traditional CNNs such as U-Net [27] and HRNet [28]. U-Net [27], due to its symmetric encoder-decoder structure, performs well in simple scenes but struggles with feature confusion in complex backgrounds, leading to a decline in segmentation accuracy. On the other hand, HRNet [28] maintains high-resolution feature maps, effectively capturing multi-scale information. While this improves segmentation performance to some extent, its ability to extract local features is still insufficient when dealing with highly similar objects. After introducing Swin Transformer [17] as the backbone network, ShadeNet significantly improves the capture of global features through the self-attention mechanism, enabling better handling of long-range dependencies and complex backgrounds. Building on this, ShadeNet further integrates a -channel attention mechanism, which dynamically adjusts the spatial and channel weights of the feature maps, enhancing the focus on key regions and detailed information. This allows ShadeNet to more  
Figure 12: Precision comparison of various network models. The horizontal axis represents different network models, and the vertical axis represents the mean Intersection over Union (mIoU) values in percentage. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mIOU (\%)** \\ \hline PSPNet & 85.05 \\ SegNet & 86.17 \\ U-Net & 87.16 \\ DeepLabV3+ & 87.97 \\ HRNet & 88.14 \\ SegFormer & 89.67 \\ ShadeNet (our) & 92.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The mIoU results of various models. accurately capture subtle feature differences between black plastic greenhouses and similar objects in complex scenes, such as road edges, effectively avoiding misclassifications. In the dark-colored water body scenario shown in Figure 13b, ShadeNet's GCLSA module, through multi-scale feature extraction, precisely captures the subtle feature differences between the water bodies and shade houses at the boundary, significantly improving segmentation accuracy. While Segformer [46] excels at capturing global features, its performance in detail handling is still inferior to ShadeNet's channel-spatial attention mechanism, resulting in reduced segmentation accuracy at boundaries. In the dark-colored vegetation scenario shown in Figure 13c, ShadeNet, by enhancing the selectivity of feature signals, more effectively distinguishes between shade houses and dark-colored vegetation, significantly reducing the misclassification of other models in high-similarity target regions. U-Net [27] and HRNet [28] exhibit a higher misclassification rate in such regions due to their feature fusion strategies failing to adequately differentiate  
Figure 13: Visualization of results from different models.