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DG-GAN:一种高质量的缺陷图像生成方法用于缺陷检测.

Xiangjie He1, Zhongqiang Luo1,2, Quanyang Li1

  • 1School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.

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PubMed
概括
此摘要是机器生成的。

使用DG-GAN生成高质量的表面缺陷图像,解决了工业制造业的数据稀缺问题. 这种方法增强了缺陷检测模型的训练,提高了准确性和稳定性.

关键词:
深度学习是一种深度学习.发现缺陷检测检测缺陷检测缺陷图像生成的缺陷图像生成产生对抗性的网络.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机视觉 计算机视觉
  • 制造业 工程 制造工程

背景情况:

  • 表面缺陷检测对于工业产品的质量,安全和效率至关重要.
  • 缺陷图像样本不足阻碍了有效的缺陷检测模型的培训.
  • 现有的方法与数据稀缺性作斗争,影响模型性能.

研究的目的:

  • 提出一种新的缺陷图像生成方法,DG-GAN,以应对有限缺陷样本数据的挑战.
  • 提高缺陷检测网络的培训稳定性和生成能力.
  • 通过生成数据,提高缺陷检测模型的准确性和趋同性.

主要方法:

  • 开发了DG-GAN,这是一个用于缺陷图像合成的渐进型生成对抗网络.
  • 包含D2对抗损失,循环一致性损失,数据增强模块和自我注意机制.
  • 在两个数据集上验证了生成的图像的质量和多样性,以及它们对YOLOX检测模型的影响.

主要成果:

  • DG-GAN生成了高质量,多样化的表面缺陷图像,显著降低了FID分数 (平均降低16.17和20.06).
  • 训练用DG-GAN生成的图像检测缺陷模型,提高了融合稳定性和检测准确性.
  • 随着添加生成的缺陷图像,YOLOX检测准确度显著增加 (高达6.1%和20.4%).

结论:

  • DG-GAN有效地生成真实的表面缺陷图像,克服工业环境中的数据限制.
  • 生成的图像可以作为有价值的训练数据,提高缺陷检测系统的性能.
  • 总局GAN显示了提高工业表面缺陷检测任务的巨大潜力.