Boundary-aware Shadow Detection via Mask Decoupling and Feature Correction

Jueyu Chen, Guanyu Xing, Jingwei Liao, Housheng Wei, Yanli Liu

Abstract: It is well-recognized that correctly detecting shadow boundaries is much more difficult than shadow interior pixels in real-world situations due to the rapid intensity and color transitions near shadow boundaries and the complex background textures. This paper presents a novel boundary-aware shadow detection network to enhance the overall detection performance, especially on challenging shadow boundaries. To enable the network aware of shadow boundaries and balance the supervision between image pixels on the boundary and inside of shadows, the shadow mask is decoupled into a body mask and boundary mask. The feature correction module is designed after feature interaction to eliminate various shadow inferences further. Experimental results show that the proposed network outperforms the state-of-the-art shadow detection methods on four shadow detection benchmark datasets.