Shallow triple Unet for shadow detection

Xuanquan Wu; Mengru Li; XinDong Lin; Junbin Wu; Ying Xi; Xiaoyi Jin

doi:10.1117/12.2572916

12 June 2020 Shallow triple Unet for shadow detection

Xuanquan Wu, Mengru Li, XinDong Lin, Junbin Wu, Ying Xi, Xiaoyi Jin

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 1151902 (2020) https://doi.org/10.1117/12.2572916
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

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.

Citation Download Citation

Xuanquan Wu, Mengru Li, XinDong Lin, Junbin Wu, Ying Xi, and Xiaoyi Jin "Shallow triple Unet for shadow detection", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 1151902 (12 June 2020); https://doi.org/10.1117/12.2572916

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