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定位和像素信任网络用于表面缺陷细分.

Yueyou Wang1, Zixuan Xu2, Li Mei1

  • 1Aerospace Research Institute of Materials and Processing Technology, Beijing 100076, China.

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

这项研究引入了一种新的两阶段深度学习网络,用于工业表面缺陷细分. 改进的模型提高了细分不平衡缺陷区域和细差距的准确性,提高了自动化质量保证.

关键词:
深度学习是一种深度学习.机器视觉 机器视觉 机器视觉表面缺陷细分的细分 表面缺陷细分两个阶段的模型模型.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 工业自动化 工业自动化

背景情况:

  • 对于表面缺陷细分的深度学习面临着数据不平衡和细缺陷差距过度细分的挑战.
  • 现有的方法在复杂的工业检查场景中难以保持高精度.

研究的目的:

  • 开发一个先进的深度学习网络,用于强大的表面缺陷细分.
  • 为了解决不平衡的面积分布和过度细分在缺陷检测中的局限性.

主要方法:

  • 提出了一个两阶段的图像细分网络,集成一个缺陷定位模块和一个像素信心模块.
  • 缺陷定位模块执行粗缺陷区域定位,将功能嵌入到第二阶段.
  • 像素信心模块通过分析邻近的像素分布来完善预测.

主要成果:

  • 拟议的网络在自建和公共数据集上都显示出更好的细分性能.
  • 在平均平均精度 (mPA) 和平均跨欧盟交叉点 (mIoU) 中观察到显著的收益.
  • 具体来说,在碳面料缺陷数据集上,mPA的1.58%±0.80%和mIoU的1.35%±0.77%的改善被注意到.

结论:

  • 这种新的双阶段网络有效地处理不平衡的数据和细差距细分挑战.
  • 这种方法提高了工业生产中自动化质量保证的可靠性.
  • 该方法对推进制造业的自动化检查系统有前途.