自动化学报2024，Vol.50Issue(8)：1539-1549,11.DOI:10.16383/j.aas.c230688
De-DDPM:可控、可迁移的缺陷图像生成方法
De-DDPM:A Controllable and Transferable Defect Image Generation Method
摘要
Abstract
Surface defect detection technology based on deep learning is an important application in industry and the quality of defect image dataset has a significant impact on defect detection performance.A defect image genera-tion method based on denoising diffusion probabilistic model(DDPM)is designed to address the pain points of high cost of obtaining defect samples and low amount of defect data in actual industrial production processes.This meth-od enhances the model's differential learning of defect locations and defect free backgrounds during the training process.Through the defect control module during the generation process,this method accurately controls the cat-egory,morphology,saliency and other features of generated defects.Through the background fusion module,de-fects can be migrated on different defect free backgrounds,which greatly reducing the difficulty of obtaining defect samples on new backgrounds.The experiment has verified the defect control and defect migration capabilities of the model,and its generated results can effectively expand the training dataset and improve the accuracy of down-stream defect detection tasks.关键词
数据增强/数据集扩充/缺陷图像生成/深度学习Key words
Data augmentation/dataset expansion/defect image generation/deep learning引用本文复制引用
岳忠牧,张喆,吕武,赵瑞祥,马杰..De-DDPM:可控、可迁移的缺陷图像生成方法[J].自动化学报,2024,50(8):1539-1549,11.基金项目
国家自然科学基金(U1913602,61991412),装备预先研究基金(50911020603)资助Supported by National Natural Science Foundation of China(U1913602,61991412)and the Foundation of Equipment Pre-re-search Area(50911020603) （U1913602,61991412）
