Delving into Dark Regions for Robust Shadow Detection
Huankang Guan Ke Xu Rynson W. H. Lau
Shadow detection is a challenging task as it requires a comprehensive
understanding of shadow characteristics and global/local illumination
conditions. We observe from our experiment that state-of-the-art deep methods
tend to have higher error rates in differentiating shadow pixels from
non-shadow pixels in dark regions (ie, regions with low-intensity values). Our
key insight to this problem is that existing methods typically learn
discriminative shadow features from the whole image globally, covering the full
range of intensity values, and may not learn the subtle differences between
shadow and non-shadow pixels in dark regions. Hence, if we can design a model
to focus on a narrower range of low-intensity regions, it may be able to learn
better discriminative features for shadow detection. Inspired by this insight,
we propose a novel shadow detection approach that first learns global
contextual cues over the entire image and then zooms into the dark regions to
learn local shadow representations. To this end, we formulate an effective
dark-region recommendation (DRR) module to recommend regions of low-intensity
values, and a novel dark-aware shadow analysis (DASA) module to learn
dark-aware shadow features from the recommended dark regions. Extensive
experiments show that the proposed method outperforms the state-of-the-art
methods on three popular shadow detection datasets. Code is available at
https://github.com/guanhuankang/ShadowDetection2021.git.