Surface defect sample generation method based on GAN

Fangyi Ni; Xiaojun Wu; Jinghui Zhou; Zhichang Liu

doi:10.1117/12.2644555

12 October 2022 Surface defect sample generation method based on GAN

Fangyi Ni, Xiaojun Wu, Jinghui Zhou, Zhichang Liu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421N (2022) https://doi.org/10.1117/12.2644555
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

In order to solve the insufficiency of training data when deep learning technology is applied to surface defect detection task, a surface defect generation algorithm based on generative adversarial network (GAN) was proposed to enhance training sample data. First, a U-shaped convolutional network was designed, and a spatial adaptive normalized structure was introduced to control the mask image to generate the defect shape, and the network from defect-free image to defect image was completed. Second, a multi-layer convolutional discriminant network is designed to extract adversarial feature of the real samples and generated samples. Finally, the adversarial training loss was designed and the generative network adversarial training was completed. Through quantitative contrast experiment, it is proved that the segmentation network has better segmentation results than without data augmentation after using the surface defect generation algorithm to generate data for data augmentation.

Citation Download Citation

Fangyi Ni, Xiaojun Wu, Jinghui Zhou, and Zhichang Liu "Surface defect sample generation method based on GAN", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421N (12 October 2022); https://doi.org/10.1117/12.2644555

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Gallium nitride

LCDs

Convolution

Defect detection

Image segmentation

Network architectures

Image quality

Show All Keywords

Keywords/Phrases

Search In:

Publication Years