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内部线程缺陷生成算法和检测系统基于生成对抗网络,你只看一次.

Zhihao Jiang1, Xiaohan Dou1, Xiaolong Liu1

  • 1School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China.

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概括
此摘要是机器生成的。

本研究引入了一种新的方法,用于检测内部线程缺陷,使用生成对抗网络 (GAN) 来增强数据,并使用YOLO算法进行检测,提高工业检查准确度.

关键词:
深度学习是一种深度学习.检测缺陷检测检测缺陷检测的方法内部线程是指内部线程.机器视觉 机器视觉 机器视觉

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科学领域:

  • 工业检查 工业检查 工业检查 工业检查
  • 机械工程 机械工程
  • 计算机视觉 计算机视觉 计算机视觉

背景情况:

  • 准确检测线程质量对于工业环境中的机械性能至关重要.
  • 目前的机器视觉方法由于几何复杂性和有限的训练数据,难以检测内部线程缺陷.
  • 挑战包括有效的检测和获得足够多的高质量的培训样本.

研究的目的:

  • 开发一种有效的机器视觉方法,用于内部线程缺陷检测.
  • 解决现有方法中培训数据不足的局限性.
  • 提高工业线程质量检查的准确性和效率.

主要方法:

  • 为内部线程提出了一种新的图像获取结构.
  • 使用生成对抗网络 (GAN) 开发了一个数据增强算法,以创建高质量的训练数据集.
  • 采用YOLO (你只看一次) 算法来检测缺陷.
  • 进行了多指标评估,并将结果与外部线程进行了比较.

主要成果:

  • 使用GANs实现了高相似性的内部线程图像生成.
  • 对于内部线程 (94.27%) 和外部线程 (93.92%),YOLO算法显示了高检测准确度.
  • 有效地识别了内部线程缺陷,克服了以前的限制.

结论:

  • 拟议的方法结合了GAN用于数据增强和YOLO用于检测,显著提高了内部线程缺陷检测.
  • 这种方法为工业检查提供了强大的解决方案,改善了机械性能保证.
  • 该技术提供了一种可行且准确的方法,用于在涉及线程的制造过程中进行质量控制.