## 4 Experiments and Results
### Ablation Experiment and Result
In the road and shade house scenes presented in Figure 11a, Mask2 Former (ResNet), Mask2 Former (Swin Transformer), and ShadeNet exhibit clear performance differences. In complex backgrounds, ShadeNet performs exceptionally well, effectively distinguishing between shade houses and roads, thereby avoiding misclassification and feature confusion, and significantly outperforming the traditional Mask2 Former (ResNet). Due to its relatively simple structure, Mask2 Former (ResNet) tends to confuse shade houses with the background in complex scenes, leading to a decrease in segmentation accuracy. Although Mask2 Former (Swin Transformer) leverages the self-attention mechanism to capture global information and shows certain advantages in handling complex scenarios, it still encounters some misclassification issues in processing finer details and boundaries. In the scene with dark-colored water bodies (Figure 11b), ShadeNet demonstrates strong discriminative ability, effectively distinguishing between shade houses and dark-colored water regions, significantly reducing misclassification. In contrast, the Mask2 Former (ResNet) tends to confuse shade houses with dark-colored water bodies in this scenario, leading to a decrease in segmentation accuracy. Although the Mask2 Former  
Figure 11: Visualization of results for the ablation experiment. Each column (**a-g**) represents a different scene, and each row represents a different model: (**a**) roads and shade houses in complex backgrounds; (**b**) shade houses and dark-colored water bodies; (**c**) shade houses and dark-colored vegetation; (**d**) shade houses and similar objects (e.g., black roofs and shade nets); (**e**) shade houses and dark lands, black plastic films; (**f**) high-density distribution of shade houses and warehouses; (**g**) shade houses in complex shadowed scenes. (Swin Transformer) shows improvements over the baseline model, it still exhibits some deficiencies in segmentation accuracy at the boundaries of the shade houses. In the scene with shade houses and dark-colored vegetation (Figure 11c), ShadeNet is able to accurately distinguish between shade houses and dark-colored vegetation, significantly reducing misclassification in high-similarity target areas compared to other models. In contrast, both Mask2 Former (ResNet) and Mask2 Former (SwinTransformer) mistakenly classify a small portion of dark-colored vegetation at the image's edge as a shade house. Despite the dark vegetation only representing a small part of its overall structure, ShadeNet is still able to accurately differentiate between the shade house and vegetation in this case, demonstrating its exceptional capability in fine-grained feature extraction. In the scene with similar objects, such as the black roofs and shade houses shown in Figure 11d, ShadeNet, with its enhanced spatial and channel features, significantly improves the model's ability to perceive both local features and global relationships in complex backgrounds, ensuring the accuracy of shade house region segmentation and the integrity of boundaries. In contrast, while Mask2 Former (SwinTransformer) shows improvements in capturing global features, it still lags behind ShadeNet in terms of detail handling and boundary integrity, resulting in poor performance of complex object segmentation. Mask2 Former (ResNet), on the other hand, exhibits significant misclassification over a larger area. In the scene with shade houses and dark land or black plastic films (Figure 11e), ShadeNet effectively distinguishes between shade houses and dark land or plastic films, particularly excelling in cases where the ground and shade house boundaries are ambiguous. In contrast, Mask2 Former (ResNet) exhibits a higher number of misclassifications in this scenario, especially in areas where the ground color closely matches black, making it difficult to accurately differentiate between the shade house and the background. Although Mask2 Former (SwinTransformer) reduces the misclassification range, it still encounters errors in some complex ground areas.