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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1265-1269,1280

Real-time semantic segmentation network based on multi-level context guidance

Wen Kai1a,1b
Xiong Junchen1a,1b
Zou Wei2
Tang Weiwei1a,1b
1. a. School of Communication & Information Engineering, b. Research Center of New Telecommunication Technology Applications, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
2. Chongqing Productivity Council, Chongqing 400021, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0335

Abstract

Aiming at the problem of low segmentation accuracy caused by ignoring its essence in real-time semantic segmentation methods, this paper proposed a multi-level context guided lightweight network. Firstly, it combined the deep-wise separable convolution with asymmetric convolution, and designed a context guided model based on the parallel asymmetric convolution to learn the joint features of local feature and its surrounding contexts. Furthermore, this research not only stacked the model in the network to achieve multi-level feature optimization, but also selected the shallow features consistent with higher-stage semantics by channel attention model, so as to improve the segmentation accuracy. According to the experimental results, the average intersection and union ratio of the proposed network on Cityscapes datasets is 72.4% at 94.7 frame rate, and this network achieves significant performance improvement on CamVid datasets. In comparison to the other current real-time semantic segmentation methods, the performance of the proposed network is better.

Foundation Support

重庆市技术预见与制度创新基金资助项目(cstc2020jsyj-yzysbAX0002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0335
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Technology of Graphic & Image
Pages: 1265-1269,1280
Serial Number: 1001-3695(2022)04-052-1265-05

Publish History

[2021-11-22] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

文凯, 熊俊臣, 邹伟, 等. 基于多级上下文引导的实时语义分割网络 [J]. 计算机应用研究, 2022, 39 (4): 1265-1269,1280. (Wen Kai, Xiong Junchen, Zou Wei, et al. Real-time semantic segmentation network based on multi-level context guidance [J]. Application Research of Computers, 2022, 39 (4): 1265-1269,1280. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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