De-DDPM:可控、可迁移的缺陷图像生成方法OA北大核心CSTPCD

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.