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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2540-2545

Deep semi-supervised learning approach for few-shot segmentation of surface defects on metal workpieces

Xu Xingyu
Zhong Yuzhong
Tu Haiyan
Dian Songyi
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0560

Abstract

In response to the scarcity of defect samples in industrial applications, this paper proposed a method for segmenting surface defects in metal workpieces with only a minimal number of required defect samples. The method combined image generation techniques with a semi-supervised learning strategy. It utilized small-sized defect patches, and extracted from a minimal number of defect images to train a defect generation model. Subsequently, the method integrated these generated defect images into normal images to facilitate data augmentation. Additionally, the method applied a semi-supervised learning strategy to train the segmentation network, aiming to mitigate the adverse effects of differences between generated and real data distributions. The experimental phase involved conducting tests on a real-world computer vision detection system for metal workpieces. The results demonstrate that the semi-supervised training strategy significantly enhances the segmentation model's generalization ability to real defects. The method achieves high segmentation accuracy using only five defect sample images.

Foundation Support

国家重点研发计划资助项目(2020YFB1709705)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0560
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Technology of Graphic & Image
Pages: 2540-2545
Serial Number: 1001-3695(2024)08-042-2540-06

Publish History

[2024-03-11] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

徐兴宇, 钟羽中, 涂海燕, 等. 基于深度半监督学习的小样本金属工件表面缺陷分割 [J]. 计算机应用研究, 2024, 41 (8): 2540-2545. (Xu Xingyu, Zhong Yuzhong, Tu Haiyan, et al. Deep semi-supervised learning approach for few-shot segmentation of surface defects on metal workpieces [J]. Application Research of Computers, 2024, 41 (8): 2540-2545. )

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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