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

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.