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基于混合增强的多颗粒度时空表示学习的接缺陷检测模型.

Chenbo Shi1, Shaojia Yan1, Lei Wang1

  • 1College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于自动接中实时化池监测的新算法,通过准确识别缺陷来加强质量控制,并改善智能制造的模型解释性.

关键词:
深度学习是一种深度学习.图像干扰 图像干扰关键是无干扰的框架.多颗粒度的时间空间特征.毛孔性缺陷 毛孔性缺陷

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 机器人和自动化 机器人和自动化
  • 计算机视觉和机器学习

背景情况:

  • 实时池监控对于高质量的自动接至关重要.
  • 现有的方法面临着图像干扰 (例如,喷雾反射) 和有限的深度学习解释能力的挑战.
  • 精确的缺陷检测,例如区分喷雾和孔隙,对于智能接系统至关重要.

研究的目的:

  • 在复杂的接场景中开发一个可靠的算法来实时监测化池质量.
  • 解决化池图像中的干扰问题,并提高深度学习模型的可解释性.
  • 为实现智能自动接的准确性,推断速度和可解释性之间的平衡.

主要方法:

  • 提出了一个多颗粒度的时空表示学习算法,将手工制作和深度学习功能结合起来.
  • 利用一个带有时间转移模块 (TSM) 的MobileNetV2骨干来捕获动态化池特征和时间信息.
  • 实现了基于注意力的多颗粒度特征聚合模块,具有跨框架注意力和卷积块注意力模块 (CBAM).
  • 集成的手工制作的面向梯度 (HOG) 和尺度不变特征转换 (SIFT) 组图功能,以提高可解释性.

主要成果:

  • 在用于化池质量监测的自构建数据集上实现了99.187%的高准确性.
  • 在搭载Intel i9 CPU和RTX 3060 GPU的平台上,实现了每样本20.983ms的实时推断速度.
  • 在拟议的方法中证明了准确性,速度和可解释性的有效平衡.

结论:

  • 拟议的混合功能学习算法有效地解决了化池监控中的干扰和可解释性问题.
  • 该方法可以为智能自动接系统提供准确和高效的实时质量评估.
  • 这种方法为推进自动化接技术的能力提供了一个有希望的解决方案.