Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 161-165.doi: 10.11896/JsJkx.191200127

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Global Bilateral Segmentation Network for Segmantic Segmentation

REN Tian-ci1, HUANG Xiang-sheng2, DING Wei-li1, AN Chong-yang1 and ZHAI Peng-bo3   

  1. 1 Institute of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066000,China
    2 Institute of Automation,Chinese Academy of Sciences,BeiJing 100190,China
    3 Institute of Microelectronics,Chinese Academy of Sciences,BeiJing 100029,China
  • Published:2020-07-07
  • About author:REN Tian-ci, born in 1995, postgradua-te.His main research interests include computer vision and pattern recognition.
    DING Wei-li, born in 1979, Ph.D, professor, Ph.D supervisor, is a member of China Computer Federation.Her main research interests include computer vision, pattern recognition and human-computer interaction.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2018YFB1308302,2018YFB1308300) and Research on Key Technologies of texture acquisition and 3D reconstruction of complex obJect surface (61573356).

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Abstract: The task of semantic segmentation is to predict the obJects according to the category at the pixel level.The difficulty lies in retaining enough spatial information and obtaining enough context information.In order to solve this problem,this paper proposes a global bilateral network semantic segmentation algorithm.In this algorithm,the large-scale convolution kernel is integrated into the BiSeNet Network,and the global path branches are added to the original spatial path and context path of the BiSeNet Network,so that the network can capture more context information.At the same time,the global pooling module in the attention optimization module and feature fusion module is replaced by the global convolution module to further improve the network acquisition.The experimental results show that the algorithm improves the MIoU index by 0.84% on Cityscaps dataset,and achieves better performance than BiSeNet Network.

Key words: Bilateral segmentation network, Global convolutional network, Semantic segmentation

CLC Number: 

  • TP389.1
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