广东工业大学学报2025，Vol.42Issue(3)：27-35,9.DOI:10.12052/gdutxb.240056
基于生成式样本合成的工件缺陷样本数据增强
Method for Data Augmentation of Workpiece Defect Samples Based on Generative Sample Synthesis
摘要
Abstract
To address the problem of severe lack of defect data in workpieces to train the deep-learning-based defect visual detection systems,this paper introduces a generative sample synthesis method that integrates generative adversarial networks(GAN)with a physical-based rendering(PBR)pipeline for data augmentation.The method employs ConSinGAN as the defect feature generation model and enhances the discriminator by incorporating a coordinate attention(CA)mechanism,enabling more precise identification of defect features in images.Additionally,the loss function is adjusted by introducing a weighted combination of reconstruction loss and multi-scale structural similarity loss to alleviate the gradient vanishing in small sample training and improve the quality of generated samples.The PBR pipeline is used to output the augmented samples,which first constructs a 3D model for the workpiece to be augmented,and then use poisson blending to merge the generated defect features with the original model texture.Finally,defect samples of the workpiece are rendered in a simulated production environment using a virtual camera.Experimental results on public datasets demonstrate the effectiveness of the proposed method in augmenting small samples of workpiece defects.关键词
数据增强/生成对抗网络/图像生成/样本合成/工件缺陷Key words
data augmentation/generative adversarial network/image generation/sample synthesis/workpiece defect分类
信息技术与安全科学引用本文复制引用
李晋芳,肖立宝,何明桐,莫建清..基于生成式样本合成的工件缺陷样本数据增强[J].广东工业大学学报,2025,42(3):27-35,9.基金项目
国家重点研发项目(2018YFB1004902) （2018YFB1004902）
广州市科技计划项目(2023A03J0724) （2023A03J0724）
广州市科技计划重点研发项目(202206010130) （202206010130）
