Improved U-NET Semantic Segmentation Network

被引:0
作者
Gao, Xueyan [1 ]
Fang, Lijin [1 ,2 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
[2] Northeastern Univ, Fac Robot Sci & Engn, Shenyang 110819, Peoples R China
来源
PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE | 2020年
关键词
U-NET; Neural network; Residual network; Attention mechanism;
D O I
10.23919/ccc50068.2020.9188804
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to enable neural networks to better process images, this paper proposed a network with residual network and attention mechanism (AM). The residual network solves the shortcomings of insufficient depth of U-NET network to a certain extent. The attention mechanism can adaptively fuse processed context information and place different attentions for different objects and shapes, instead of simply aggregating all processed information. The method proposed in this paper is compared with the network using the residual network alone and the network using the attention mechanism alone. The experimental results show that the network proposed in this paper performs better.
引用
收藏
页码:7090 / 7095
页数:6
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