Image semantic segmentation with an improved fully convolutional network

Kuo Kun Tseng, Haichuan Sun, Junwu Liu, Jiaqi Li, K. L. Yung, W. H. Ip

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


With the development of deep learning and the emergence of unmanned driving, fully convolutional networks are a feasible and effective for image semantic segmentation. DeepLab is an algorithm based on the fully convolutional networks. However, DeepLab algorithm still has room for improvement, and we design three improved methods: (1) the global context structure module, (2) highly efficient decoder module, and (3) multi-scale feature fusion module. The experimental results show that the three improved methods that we proposed in this paper can make the model obtain more expressive features and improve the accuracy of the algorithm. At the same time, we do some experiments on the Cityscapes dataset to further verify robustness and effectiveness of the improved algorithm. Finally, the improved algorithm is applied to the actual scene and has certain practical value.

Original languageEnglish
Pages (from-to)8253-8273
Number of pages21
JournalSoft Computing
Issue number11
Publication statusPublished - 1 Jun 2020


  • Decoder module
  • Fully convolutional networks
  • Global context structure
  • Image semantic segmentation
  • Multi-scale feature fusion

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology


Dive into the research topics of 'Image semantic segmentation with an improved fully convolutional network'. Together they form a unique fingerprint.

Cite this