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12 June 2020 Shallow triple Unet for shadow detection
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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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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