计算机与现代化

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基于点云的转子表面缺陷检测方法

  

  1. (南开大学计算机学院,天津300350)
  • 收稿日期:2019-07-22 出版日期:2019-10-28 发布日期:2019-10-29
  • 作者简介:李宇萌(1996-),女,河北保定人,硕士研究生,研究方向:计算机视觉,E-mail: 651182619@qq.com。
  • 基金资助:
    天津市智能制造专项资金资助项目 (201707105, 201707108, 201810602, 201907206, 201907210); 天津市互联网先进制造专项资金资助项目(18ZXRHGX00110)

A Rotor Surface Defect Detection Method Based on Point Cloud

  1. (College of Computer Science, Nankai University, Tianjin 300350, China)
  • Received:2019-07-22 Online:2019-10-28 Published:2019-10-29

摘要: 转子缺陷影响鼓风机运转,降低工作性能,为工业生产带来安全隐患。传统人工检测费时费力,检测和标注精度低,且难以进行缺陷精准分类。因此,本文基于机器视觉获取点云数据,对其进行预处理,并对比基于点云配准和基于工件特征的2种缺陷检测方法。实验结果表明,基于工件特征的缺陷检测能得到更精确的缺陷标注和分类效果,并为缺陷检测方法研究工作提供了新的方向。

关键词: 机器视觉, 点云数据, 缺陷检测, 点云配准, 工件特征

Abstract: Rotor defects affect the operation of blower, reduce the working performance and bring safety risks for industrial production. Traditional manual detection is time-consuming and laborious, with low detection and marking accuracy, and it is difficult to accurately classify defects. Therefore, this paper obtains point cloud data based on machine vision, preprocesses it, and compares two defect detection methods respectively based on point cloud registration and workpiece features. The experimental results show that the defect detection based on workpiece features can get more accurate results of defect labeling and classification, and provide a new direction for the research of defect detection methods.

Key words: machine vision, point cloud data, defect detection, point cloud registration, artifact features

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