Abstract
Shadow detection is a fundamental and challenging task,
since it requires an understanding of global image semantics and there are various backgrounds around shadows.
This paper presents a novel network for shadow detection
by analyzing image context in a direction-aware manner.
To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network
(RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these
weights through training, we can recover direction-aware
spatial context (DSC) for detecting shadows. This design is
developed into the DSC module and embedded in a CNN to
learn DSC features at different levels. Moreover, a weighted
cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate
our network. Experimental results show that our network
outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate