Defect Image Sample Generation With Diffusion Prior for Steel Surface Defect Recognition

arXiv:2405.01872v1 Announce Type: new
Abstract: The task of steel surface defect recognition is an industrial problem with great industry values. The data insufficiency is the major challenge in training a robust defect recognition network. Existing methods have investigated to enlarge the dataset by generating samples with generative models. However, their generation quality is still limited by the insufficiency of defect image samples. To this end, we propose Stable Surface Defect Generation (StableSDG), which transfers the vast generation distribution embedded in …

abstract arxiv challenge cs.cv data dataset diffusion generative generative models image industrial industry major network prior recognition robust sample samples surface training type values