双流网络一类分类模型用于缺陷检查
Seunghun Lee1, Chenglong Luo1, Sungkwan Lee2
1Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种新的工业缺陷检查的一类分类方法,有效处理不平衡的数据. 拟议的双流网络显著提高了检测汽车零部件接缺陷的准确性.
科学领域:
- 工业制造业 工业制造业 工业制造业
- 人工智能的人工智能
- 机器视觉 机器视觉 机器视觉
背景情况:
- 缺陷检查对于保持制造中的质量和效率至关重要.
- 由人工智能驱动的机器视觉系统显示出希望,但与不平衡的数据集作斗争.
- 数据不平衡是工业缺陷检测的一个常见挑战.
研究的目的:
- 为不平衡的数据集提出一种使用一类分类 (OCC) 模型的缺陷检查方法.
- 开发一个双流网络架构来解决OCC中的表示崩问题.
- 增强OCC模型的决策边界,以防止对训练数据的崩.
主要方法:
- 一个新的双流网络架构,集成全球和本地特征提取器.
- 将面向对象的不变特征与面向训练数据的局部特征相结合.
- 应用于汽车安全气囊支架接缺陷检查,使用现实世界和实验室数据.
主要成果:
- 拟议的双流OCC模型显示了比以前的方法更好的性能.
- 在准确度 (高达8.19%),精度 (高达10.74%) 和F1得分 (高达4.02%) 中显著提高.
- 分析澄清了分类层和网络架构对检查准确性的影响.
结论:
- 拟议的双流OCC模型有效处理工业缺陷检查中的不平衡数据.
- 该方法为制造业的质量控制提供了强大的解决方案,汽车接检查就是一个例子.
- 这种方法成功地减轻了代表性的崩,并建立了一个更合适的决策边界.
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