DDPM-MoCo：利用生成式与对比学习推进工业表面缺陷生成与检测
DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning
AI总结：
本文提出DDPM-MoCo方法，结合扩散概率模型生成缺陷样本与动量对比学习训练无标签数据，提升金属表面缺陷检测效果。
摘要：
基于深度学习的工业检测任务通常需要解决两个问题：（1）获取充足且有效的数据样本，（2）采用高效便捷的模型训练方法。本文提出了一种新颖的缺陷生成方法，名为DDPM-MoCo，以应对这些问题。首先，我们利用去噪扩散概率模型（DDPM）生成高质量的缺陷数据样本，克服模型学习中样本数据不足的问题。此外，我们利用无监督学习动量对比模型（MoCo），并采用增强的批量对比损失函数，在无标签数据上训练模型，解决扩散模型训练过程中大规模负样本编码的效率和一致性问题。实验结果表明，该方法实现了一种增强的金属表面缺陷视觉检测方法，覆盖了从生成用于训练扩散模型的无标签样本数据，到利用相同的带标签样本数据进行下游检测任务的完整流程。该研究为金属加工行业中的视觉检测提供了有价值的实践见解和应用潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
