电工电气杂志社

基于3D点云数据的产品缺陷检测研究

来源：电工电气发布时间：2023-02-06 14:06 浏览次数：542

基于3D点云数据的产品缺陷检测研究

李潮林¹，陈仲生^1,2，左旺¹，侯幸林²

(1 湖南工业大学电气与信息工程学院，湖南株洲 412007；

2 常州工学院汽车工程学院，江苏常州 213032)

摘要：传统 2D 视觉检测技术存在效率低下、检测精确度较低等不足，3D 视觉技术因能显著提高缺陷检测的效率和可靠性得到了高度关注和广泛研究。对已有文献进行了广泛调研分析，介绍了 3D 点云数据的基本概念、获取方式及其预处理方法，重点归纳了传统点云数据缺陷检测方法和点云数据深度学习缺陷检测方法，并探讨了当前研究中存在的问题与挑战。

关键词： 3D 视觉；缺陷检测；点云数据

中图分类号：TP391.41 文献标识码：A 文章编号：1007-3175(2023)01-0048-07

Research on Product Defect Detection Based on 3D Point Cloud Data

LI Chao-lin¹, CHEN Zhong-sheng^1,2, ZUO Wang¹, HOU Xing-lin²

（1 School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China;

2 School of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China）

Abstract: The traditional 2D vision detection technology has disadvantages of low efficiency and detection accuracy, while 3D vision technology can significantly improve its detection efficiency and reliability, so it has been paid high attention and widely analyzed. After making extensive analysis of the existing literature, the paper introduces the basic concept, access and pretreatment method of 3D point cloud data, summarizes the traditional point cloud data defect detection method and point cloud data deep learning defect detection method, and finally discusses the problems and challenges of the current research.

Key words: 3D vision; defect detection; point cloud data

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