自动化学报2025，Vol.51Issue(7)：1423-1462,40.DOI:10.16383/j.aas.c240139
工业外观检测中的图像扩增方法综述
A Review of Image Augmentation Methods in Industrial Cosmetic Inspection
摘要
Abstract
Image augmentation is a commonly used data processing method in industrial cosmetic inspection,which improves the generalization of detection models and prevents overfitting.Based on the different sources of augment-ation results,current industrial image augmentation methods are categorized into traditional transformation-based and model generation-based.The former includes image space-based and feature space-based methods.The latter is classified into unconditional,low-dimensional conditional,and image conditional methods based on different input conditional information of models.The principles,application effects,advantages,and disadvantages of related methods are analyzed,focusing on model generation-based augmentation methods such as those based on generat-ive adversarial networks and diffusion models.Furthermore,the relevant works on the three types of model genera-tion-based methods are categorized according to the type of annotations for augmentation results and the technical characteristics of the methods.A multidimensional table is used to elaborate on the research details of various methods,followed by comprehensive analyses of their base models,evaluation metrics,and augmentation perform-ance.Finally,the paper summarizes the current challenges in industrial image augmentation and provides an out-look on future development directions.关键词
图像扩增/图像生成/生成对抗网络/扩散模型/表面缺陷检测/计算机视觉Key words
Image augmentation/image generation/generative adversarial networks/diffusion models/surface de-fect inspection/computer vision引用本文复制引用
魏静,史庆丰,沈飞,张正涛,陶显,罗惠元..工业外观检测中的图像扩增方法综述[J].自动化学报,2025,51(7):1423-1462,40.基金项目
国家重点研发计划项目(2022YFB3303800),北京市自然科学基金-小米创新联合基金(L243018),中国科学院青年创新促进会(2020139)资助Supported by National Key Research and Development Pro-gram of China(2022YFB3303800),Beijing Municipal Natural Science Foundation-Xiaomi Joint Innovation Fund of China(L243018),and Youth Innovation Promotion Association of Chinese Academy of Sciences(2020139) （2022YFB3303800）
