Deep learning models like U-Net and its variants, have established state-of-the-art performance in edge detection tasks and are used by Generative AI services world-wide for their image generation models. However, their decision-making processes remain opaque, operating as "black boxes" that obscure the rationale behind specific boundary predictions. This lack of transparency is a critical barrier in safety-critical applications where verification is mandatory. To bridge the gap between high-performance deep learning and interpretable logic, we propose the Rule-Based Spatial Mixture-of-Experts U-Net (sMoE U-Net). Our architecture introduces two key innovations: (1) Spatially-Adaptive Mixture-of-Experts (sMoE) blocks integrated into the decoder skip connections, which dynamically gate between "Context" (smooth) and "Boundary" (sharp) experts based on local feature statistics; and (2) a Takagi-Sugeno-Kang (TSK) Fuzzy Head that replaces the standard classification layer. This fuzzy head fuses deep semantic features with heuristic edge signals using explicit IF-THEN rules. We evaluate our method on the BSDS500 benchmark, achieving an Optimal Dataset Scale (ODS) F-score of 0.7628, effectively matching purely deep baselines like HED (0.7688) while outperforming the standard U-Net (0.7437). Crucially, our model provides pixel-level explainability through "Rule Firing Maps" and "Strategy Maps," allowing users to visualize whether an edge was detected due to strong gradients, high semantic confidence, or specific logical rule combinations.

翻译：U-Net及其变体等深度学习模型已在边缘检测任务中达到最先进的性能，并被全球生成式AI服务广泛用于图像生成模型。然而，其决策过程仍不透明，如同"黑箱"般遮蔽了特定边界预测的内在逻辑。这种透明度的缺失在需要强制验证的安全关键应用中构成关键障碍。为弥合高性能深度学习与可解释逻辑之间的鸿沟，我们提出基于规则的空间专家混合U-Net（sMoE U-Net）。该架构包含两项关键创新：（1）集成于解码器跳跃连接中的空间自适应专家混合（sMoE）模块，能根据局部特征统计量在"上下文"（平滑）与"边界"（锐化）专家间进行动态门控选择；（2）替代标准分类层的Takagi-Sugeno-Kang（TSK）模糊头部，该模块通过显式IF-THEN规则将深层语义特征与启发式边缘信号相融合。我们在BSDS500基准上评估所提方法，获得0.7628的最优数据集尺度（ODS）F分数，在有效匹配HED（0.7688）等纯深度学习基线的同时，显著超越标准U-Net（0.7437）。更重要的是，本模型通过"规则触发图"与"策略图"提供像素级可解释性，使用户能直观判别边缘检测是源于强梯度响应、高语义置信度还是特定逻辑规则组合。