面向雪糕板深度缺陷检测的三维点云分割算法  

3D Point Cloud Segmentation Algorithm Using in Depth Defect Detection of Ice Cream Board

作　　者：刘月 白福忠[1] 李萍[2] LIU Yue;BAI Fu-zhong;LI Ping(School of Mechanical Engineering,Inner Mongolia University of Technology,Hohhot 010051,China;Beijing Polytechnic College,Beijing 100042,China)

机构地区：[1]内蒙古工业大学机械工程学院,内蒙古呼和浩特010051 [2]北京工业职业技术学院,北京100042

出　　处：《光学与光电技术》2024年第6期86-92,共7页Optics & Optoelectronic Technology

基　　金：内蒙古科技计划项目(2021GG0263);内蒙古自治区直属高校基本科研业务费项目(JY20220081、JY20230039)资助。

摘　　要：雪糕板表面三维点云的准确分割是雪糕板缺陷识别与检测的基础。提出了一种适用于多种深度缺陷类型雪糕板的三维点云分割算法,首先提取雪糕板表面断面数据,通过基准直线拟合、使用标准差判据来识别雪糕板属于弓弯类或近似平面类;然后对于这两类目标,分别利用随机采样一致性(RANSAC)算法或迭代最近点(ICP)配准算法实现雪糕板点云分割。实验结果表明,提出的方法能够可靠地对四类典型目标进行有效的区域分割,其分割准确度高于94.6%,完整度高于91.5%,为后续缺陷识别和定量检测奠定基础。该算法能够有效提升木质板材平面分割精度,具有实际工程应用价值。Accurate segmentation of three-dimensional(3D)point cloud on the ice cream board surface is the basis of defect identification and detection for ice cream board.A 3D point cloud segmentation algorithm for ice cream board with various deep defect types is proposed.Firstly,the cross-section data of ice cream board surface is extracted,and the ice cream board types about warp or near-plane are identified by line fitting and the standard deviation criterion.Then,for these two kinds of targets,random sampling consistency(RANSAC)algorithm or iterative nearest point(ICP)registration algorithm are used to achieve point cloud segmentation.The experimental results show that four types of typical targets can be reliably and efficiently segmented by the method presented in this paper.The segmentation accuracy is higher than 94.6%,and the integrity is higher than 91.5%,which lays a foundation for subsequent defect indentification and quantitative detection.The proposed algorithm can effectively improve the segmentation accuracy of wood plate plane and has practical engineering application value.

关 键 词：雪糕板 三维点云 点云分割 RANSAC算法 ICP配准 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TH74[自动化与计算机技术—计算机科学与技术]