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首页> 外文期刊>Optics and Lasers in Engineering >An intelligent and automated 3D surface defect detection system for quantitative 3D estimation and feature classification of material surface defects
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An intelligent and automated 3D surface defect detection system for quantitative 3D estimation and feature classification of material surface defects

机译：用于定量3D估计的智能和自动化3D表面缺陷检测系统和材料表面缺陷的特征分类

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摘要

To evaluate defects on the surface of the materials at the 3D level accurately and quantitatively, a 3D surface defect detection system based on stereo vision is presented, which can extract the precise 3D defect features of the detected object. The proposed detection system consists of two image capture modules and a turntable to capture the complete 3D information and color texture information from the object surface. More precisely, each image capture module is a binocular stereo vision system containing two monochrome cameras, a color camera, and a speckle projector which is used to reconstruct the 3D point clouds of the object surface based on stereo digital image correlation (stereo-DIC). Furthermore, a point-image mapping relationship between the reconstructed 3D object points and the color images is established. Eventually, the 3D characteristic parameters of defects are calculated by the corresponding 3D point cloud of the defect area obtained by segmenting the defect area using the image segmentation and point cloud segmentation algorithms according to this point-image mapping relationship. A convolutional neural network named DenseNets is employed to identify defect types intelligently. A high-precision mull-camera calibration method based on close-range photogrammetry is applied to ensure system detection accuracy in the proposed system. The experimental results demonstrate that the system has higher accuracy and better performance in system calibration, 3D reconstruction, and defect feature calculation.
机译：为了准确和定量地评估在3D电平的材料表面上的缺陷，提出了一种基于立体视觉的3D表面缺陷检测系统，其可以提取检测到的对象的精确3D缺陷特征。所提出的检测系统由两个图像捕获模块和转盘组成，以从物体表面捕获完整的3D信息和颜色纹理信息。更精确地，每个图像捕获模块是包含两个单色相机，彩色相机和散斑投影仪的双目立体声视觉系统，用于基于立体声数字图像相关（立体声DIC）重建物体表面的3D点云。此外，建立重建的3D对象点与彩色图像之间的点图像映射关系。最终，通过根据该点图像映射关系将缺陷区域分割缺陷区域通过分割缺陷区域获得的相应的3D点云来计算缺陷的3D特征参数。名为Densenets的卷积神经网络被用于智能地识别缺陷类型。应用基于近距离摄影测量的高精度MPLAM相机校准方法，以确保所提出的系统中的系统检测精度。实验结果表明，系统在系统校准，3D重建和缺陷特征计算方面具有更高的准确性和更好的性能。

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  • 来源
    《Optics and Lasers in Engineering》 |2021年第9期|106633.1-106633.16|共16页
  • 作者

    Zong Yulong; Liang Jin; Wang Huan; Ren Maodong; Zhang Mingkai; Li Wenpan; Lu Wang; Ye Meitu;

  • 作者单位

    Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China|Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 71054 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China|Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 71054 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ Inst Sci & Educ Dev Xian 710049 Shaanxi Peoples R China;

    XTOP 3D Technol Shenzhen Co Ltd Innovat Lab Shenzhen 518060 Peoples R China;

    Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China|Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 71054 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China|Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 71054 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China|Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 71054 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China|Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 71054 Shaanxi Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    3D defect detection system; 3D reconstruction; Point-image relationship mapping; Image segmentation and classification; Point cloud segmentation; 3D feature calculation;

    机译：3D缺陷检测系统;3D重建;点图像关系映射;图像分割和分类;点云分割;3D特征计算;

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