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  4. Vol 8, Issue 1, 2024

Deep Data Augmentation for Defect Detection Enhancement: A Diffusion Model Based Approach

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DOI: 10.23977/acss.2024.080114 | Downloads: 9 | Views: 96

Author(s)

Yi Peng 1, Yaping Zhang 1

Affiliation(s)

1 School of Information Science and Technology, Yunnan Normal University, Kunming, Yunnan, 650500, China

Corresponding Author

Yaping Zhang

ABSTRACT

Weld defect detection is a crucial step in industrial production processes. To effectively identify these defects, the X-ray inspection method based on non-destructive testing is commonly employed. Addressing the challenges of limited sample size and class imbalance in X-ray images, this study proposes an enhanced diffusion model algorithm to augment samples, thereby improving the defect detection capability for rare categories. Experimental results prove the enhanced dataset's detection performance surpassing that of the original dataset. The improvement is notable, with a 5.1% enhancement on the WDD dataset. This paper presents a viable data augmentation solution for small-sample weld seam defect detection.

KEYWORDS

Weld defect detection, data augmentation, GAN, diffusion model

CITE THIS PAPER

Yi Peng, Yaping Zhang, Deep Data Augmentation for Defect Detection Enhancement: A Diffusion Model Based Approach. Advances in Computer, Signals and Systems (2024) Vol. 8: 122-126. DOI: http://dx.doi.org/10.23977/acss.2024.080114.

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