液晶与显示2026，Vol.41Issue(4)：523-533,11.DOI:10.37188/CJLCD.2026-0031
基于改进BEGAN的钢材缺陷图像数据增强方法
Data augmentation method for steel defect images based on improved BEGAN
摘要
Abstract
Materials science research is trying to develop deep learning-based computer vision methods,but currently limited experimental data is difficult to support the exploration of such big data-based methods.This paper proposes an improved boundary equilibrium generative adversarial network(BEGAN)data augmentation model to address this issue.Firstly,replacing the normalization method in the generator network with spectral normalization reduces the requirement for training sample size compared to batch normalization;Secondly,adding residual modules to the generator/decoder of the model avoids overfitting and accelerates model training;Finally,a self-attention mechanism is added to enhance the model's ability to extract defect details,resulting in smoother and faster convergence of loss parameters during the training process.This paper conducted ablation experiments and comparative experiments using a public dataset of steel defects.Through two evaluation metrics and classification network accuracy,the experiments demonstrated that the improved model significantly outperforms four mainstream generative models.Compared to the BEGAN model's generative dataset,the image classification performance improved by 5.55%;the FID value decreased by 54.35%;the IS value increased by 18.18%.The performance of generated specimens proved this improved method is sufficient as an image enhancement method to cope with small sample problems.关键词
钢材表面缺陷/数据增强/神经网络/生成对抗网络/自注意力机制Key words
steel surface defect/data augmentation/neural network/generative adversarial network/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
赵健宏,杨华民,隋意,王鹏..基于改进BEGAN的钢材缺陷图像数据增强方法[J].液晶与显示,2026,41(4):523-533,11.基金项目
吉林省科技创新平台建设项目(No.YDZJ202302CXJD027)Supported by Jilin Provincial Science and Technology Innovation Platform Construction Project(No.YDZJ202302CXJD027) （No.YDZJ202302CXJD027）
