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http://dx.doi.org/10.14400/JDC.2021.19.6.251

A Study on Residual U-Net for Semantic Segmentation based on Deep Learning  

Shin, Seokyong (Department of Plasma Bio Display, Kwangwoon University)
Lee, SangHun (Ingenium College of Liberal Arts, Kwangwoon University)
Han, HyunHo (College of General Education, University of Ulsan)
Publication Information
Journal of Digital Convergence / v.19, no.6, 2021 , pp. 251-258 More about this Journal
Abstract
In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.
Keywords
Deep learning; Encoder-Decoder; Image processing; Residual learning; Semantic segmentation;
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