期刊刊名：科學與工程技術期刊 　 卷期：16卷2期

篇名出版日期：2020年9月1日

作者：DENG-YUAN HUANG, JING-YAN YANG, YU-YUN WANG,黃登淵,楊靖言,王喻筠

語言：English

關鍵字：Semantic segmentation, Scene understanding, Encoder network, Decoder network,語義分割,場景理解,編碼器網路,解碼器網路

被點閱次數：1次

閱讀時間：356sec

摘要：
Semantic segmentation, also known as dense prediction, is a task in computer vision that is critical for scene understanding and commonly used for pixel-wise labeling of whole images. This paper proposes a new semantic segmentation network architecture dedicated to decoder structural design. Moreover, different mapping mechanisms are introduced as a part of the decoder network. The proposed architecture was tested on the 21 classes of the Pascal Visual Object Classes Challenge 2012 data set. The proposed method has a higher mean intersection over union score and pixel accuracy than those of the U-SegNet, UNet, and ENet models but similar results to those of the SegNet model. Additionally, the effect of using magnification methods in the decoder network on object segmentation performance was investigated.

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