Despite significant progress in shadow detection, current methods still
struggle with the adverse impact of background color, which may lead to errors
when shadows are present on complex backgrounds. Drawing inspiration from the
human visual system, we treat the input shadow image as a composition of a
background layer and a shadow layer, and design a Style-guided Dual-layer
Disentanglement Network (SDDNet) to model these layers independently. To
achieve this, we devise a Feature Separation and Recombination (FSR) module
that decomposes multi-level features into shadow-related and background-related
components by offering specialized supervision for each component, while
preserving information integrity and avoiding redundancy through the
reconstruction constraint. Moreover, we propose a Shadow Style Filter (SSF)
module to guide the feature disentanglement by focusing on style
differentiation and uniformization. With these two modules and our overall
pipeline, our model effectively minimizes the detrimental effects of background
color, yielding superior performance on three public datasets with a real-time
inference speed of 32 FPS.

通过设计了 Style-guided Dual-layer Disentanglement Network (SDDNet) 来解决当前阴影检测方法中背景颜色对结果的不利影响，并通过 Shadow Style Filter (SSF) 模块实现特征解缠并改善背景颜色的不利影响，从而提高了模型在三个公共数据集上的性能表现。