Robust classification method for printed circuit board defects based on virtual and real space cooperative diffusion model

Abstract

Printed circuit board defect detection based on deep learning is a key technology in intelligent manufacturing. However, datasets collected in industrial environments often suffer from label noise due to inconsistent expert knowledge and ambiguous defect features, which severely degrades the performance of conventional models. To address this challenge, this paper proposes a robust classification framework based on a virtual-real space collaborative diffusion model. The proposed method introduces a distributed guidance mechanism to construct a latent semantic space (virtual space), which provides probabilistic priors for the inverse diffusion process in the real image space. Furthermore, a parameterized label encoding module is designed to mitigate the loss of fine-grained semantics during forward diffusion, and an attribute interaction attention mechanism is proposed to enhance the modeling of key defect attributes. Extensive experiments on the publicly available dataset with varying levels of label noise demonstrate that our method outperforms state-of-the-art approaches in terms of classification accuracy, robustness, and generated image quality, especially under strong noise conditions.