Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
623-628,634

Weakly supervised semantic segmentation network based on boundary assistance

Yang Dawei
Chi Jinsheng
Mao Lin
College of Mechanical & Electronic Engineering, Dalian Minzu University, Dalian Liaoning 116600, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0265

Abstract

Due to the random growth mechanism of the seed region in the weakly supervised semantic segmentation task, the weakly supervised semantic segmentation network often suffers from wrong segmentation and missed segmentation problems. To address the above problems, this paper proposed a boundary-assisted weakly supervised semantic segmentation network. The network provided a reference for the growth of seed regions by utilizing boundary information and semantic information, so that the seed regions could naturally grow to the target boundary and correctly differentiated the target categories when the non-target object blocked or overlapped the target, and generated pseudo-pixel masks that could cover a more complete target. This paper used the pseudo-pixel mask as the supervisory information to train the segmentation network, which could improve the problem of missegmentation and omission by the pseudo-pixel mask that couldn't cover the target region accurately, and improved the accuracy of the weakly supervised semantic segmentation network. It evaluated the network on the generalized dataset PASCAL VOC 2012 validation and test sets, and the mIoU reaches 71.7% and 73.2%, respectively. The experimental results show that the performance of the proposed network outperforms most of the current weakly-supervised semantic segmentation methods at the image level.

Foundation Support

国家自然科学基金资助项目(61673084)
辽宁省自然科学基金资助项目(20170540192,20180550866,2020-MZLH-24)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0265
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Technology of Graphic & Image
Pages: 623-628,634
Serial Number: 1001-3695(2024)02-046-0623-06

Publish History

[2023-08-22] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

杨大伟, 迟津生, 毛琳. 基于边界辅助的弱监督语义分割网络 [J]. 计算机应用研究, 2024, 41 (2): 623-628,634. (Yang Dawei, Chi Jinsheng, Mao Lin. Weakly supervised semantic segmentation network based on boundary assistance [J]. Application Research of Computers, 2024, 41 (2): 623-628,634. )

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  • 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.

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