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优化自动化光学检查:一种自适应的融合和半监督的自我学习方法,用于在稀缺标记数据的场景中提高准确性和效率.

Yu-Shu Ni1, Wei-Lun Chen1, Yi Liu2

  • 1Department of Electronics Engineering, Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu City 300, Taiwan.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括

本研究介绍了自适应融合半监督自学 (AFSL) 方法,以提高自动光学检查 (AOI) 中使用较少标记数据的对象检测. 在缺陷检测任务中,AFSL显著提高了模型精度和效率.

关键词:
自动光学检查自动光学检查对象检测检测对象检测对象检测半监督学习 半监督学习

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 工业自动化 工业自动化

背景情况:

  • 自动光学检查 (AOI) 系统需要高的对象检测准确度来识别缺陷.
  • 传统的方法往往依赖于广泛的注释数据集,这些数据集的获取是昂贵和耗时的.
  • 有限的标记数据对开发强大的AOI模型构成重大挑战.

研究的目的:

  • 开发创新策略,以提高AOI中的对象检测精度.
  • 减少对大型注释数据集的依赖,以训练缺陷检测模型.
  • 为AOI提出和评估一种新的半监督学习方法.

主要方法:

  • 使用32个类别的3579张图像数据集开发缺陷检测模型.
  • 实施数据增强和注释精细化技术.
  • 关于自适应融合半监督自学 (AFSL) 方法的建议,该方法包含一个界限框分配器,自适应训练计划器和数据分配器.

主要成果:

  • 通过AFSL方法,COCO数据集的平均精度从43.5%提高到57.1%.
  • 在特定的AOI数据集上观察到mAP的2.6%改善.
  • 该方法在使用最小标记数据的AOI数据集上展示了增强的性能.

结论:

  • AFSL方法有效地提高了AOI中的对象检测准确性.
  • 半监督学习方法,如AFSL,对于克服AOI中的数据限制是可行的.
  • 拟议的方法为工业应用中的缺陷检测提供了更有效,更精确的解决方案.