Unveiling the potential of progressive training diffusion model for defect image generation and recognition in industrial processes

Abstract

Industrial defect image generation is a crucial technique for augmenting defect image data, catering to fulfill the exigencies of training data necessary for defect recognition methods. In recent years, generative adversarial networks have seen widespread use in generating defect images. However, constrained by the scarcity of training data and the intricate variability in various defect categories, the existing methods are susceptible to issues such as training instability, a dearth of diversity, and suboptimal image quality. To overcome these challenges, this paper proposes a defect image generation framework based on a progressive training diffusion model (PTDM). Firstly, this study adopts a denoising diffusion model, supplanting traditional generative adversarial networks, to mitigate issues related to training instability and the dearth of diversity observed in generated images. Secondly, a novel progressive training strategy based on the self-designed image quality evaluator is developed to efficiently generate numerous defect images while maintaining quality. Finally, extensive experiments on a steel surface defect image dataset were conducted to validate the performance of the proposed framework in defect image generation and recognition tasks.