Shallow triple Unet for shadow detection
Xuanquan Wu, Mengru Li, Xindong Lin, Junbin Wu, Ying Xi, Xiaoyi Jin · 2020
Shadow detection is an important part of scene understanding tasks. This paper proposes a network, named Shallow Triple Unet, using shallow Unet as a unit for shadow detection. The network structure is intuitive and the number of parameters is small. With the techniques of hierarchical supervision and results fusion, it can achieve a good shadow detection effect. In order to prove the effectiveness of the network, we performed experiments on popular SBU datasets and compared them with networks such as patched-CNN, stacked-CNN, scGAN, and DSC. The results prove that our network is the best among them, with a BER index of 5.45%. In addition, we also performed ablation experiments to verify the role of various parts of the network. Experiments show that all the techniques we use have significantly improved shadow detection results.