计算机工程与应用2024，Vol.60Issue(2)：103-112,10.DOI:10.3778/j.issn.1002-8331.2208-0326
基于NVAE和OB-Mix的小样本数据增强方法
Few Samples Data Augmentation Method Based on NVAE and OB-Mix
摘要
Abstract
Due to the high dependence of deep learning models on massive labeled data,many cutting-edge target detec-tion theories are difficult to apply to the field of industrial detection.To this end,a small-sample data augmentation method based on NVAE for image generation and OB-Mix for data regularization is proposed.The specific method is to build a data distribution model of the detection target images through NVAE,and then generate new target images that belong to the same distribution as the real target images by sampling latent variables.After the generated target images are obtained,an OB-Mix data augmentation strategy is proposed,which mixes the generated target images with the background images at random positions to construct new images data,thereby improving the localization ability and generalization ability of the network.In the case of using only 474 labeled images and 400 background images without detection targets,the detec-tion Precision of YOLOv5 reaches 95.86%,which is 17.60 percentage points higher than the training without this method.关键词
数据增强/小样本/数据生成/新派变分自编码器(NVAE)/表面缺陷检测/深度学习Key words
data augmentation/small-sample/image generation/nouveau variational auto-encoder(NVAE)/surface defect detection/deep learning分类
信息技术与安全科学引用本文复制引用
杨玮,钟名锋,杨根,侯至丞,王卫军,袁海..基于NVAE和OB-Mix的小样本数据增强方法[J].计算机工程与应用,2024,60(2):103-112,10.基金项目
国家重点研发计划(2018YFA0902903) （2018YFA0902903）
广州市基础研究计划(202102080650). （202102080650）
