  The task of industrial detection based on deep learning often involves
solving two problems: (1) obtaining sufficient and effective data samples, (2)
and using efficient and convenient model training methods. In this paper, we
introduce a novel defect-generation method, named DDPM-MoCo, to address these
issues. Firstly, we utilize the Denoising Diffusion Probabilistic Model (DDPM)
to generate high-quality defect data samples, overcoming the problem of
insufficient sample data for model learning. Furthermore, we utilize the
unsupervised learning Momentum Contrast model (MoCo) with an enhanced batch
contrastive loss function for training the model on unlabeled data, addressing
the efficiency and consistency challenges in large-scale negative sample
encoding during diffusion model training. The experimental results showcase an
enhanced visual detection method for identifying defects on metal surfaces,
covering the entire process, starting from generating unlabeled sample data for
training the diffusion model, to utilizing the same labeled sample data for
downstream detection tasks. This study offers valuable practical insights and
application potential for visual detection in the metal processing industry.
