基于机器学习的缺陷检测精度提高技术研究

Yun Chen, Zijing Wang, Shuo Sheng, G. Shi
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引用次数: 0

摘要

自动光学检测(Automated Optical Inspection, AOI)技术在工业场景中应用广泛,研究多集中在理论模型或系统设计上。在工业生产环境中,对于不同类型和规格的产品,需要根据产品缺陷的检测情况及时调整检测参数的阈值,以提高设备的检出率。本文在实际场景的研究中,对设备检测产生的缺陷数据进行收集和整理,并通过机器学习方法对不同类型缺陷的检测参数进行分析,从而提高缺陷检测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Accuracy Improvement Technology of Defect Detection Based on Machine Learning
Automated Optical Inspection (AOI) technology is widely used in industrial scenes, and the research mostly focuses on theoretical models or system design. In the industrial production environment, for different types and specifications of products, it is necessary to adjust the threshold of the detection parameters in time according to the detection of product defects, in order to improve the detection rate of the equipment. In this paper, in the study of the actual scene, the defect data generated by equipment detection is collected and collated, and the detection parameters of different types of defects are analyzed by machine learning methods, so as to improve the accuracy of defect detection.
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