MVGCN: Multi-View Graph Convolutional Neural Network for Surface Defect Identification Using Three-Dimensional Point Cloud
MVGCN: Multi-View Graph Convolutional Neural Network for Surface Defect Identification Using Three-Dimensional Point Cloud
复制标题
MVGCN：使用三维点云进行表面缺陷识别的多视图图卷积神经网络
DOI：
10.1115/1.4056005
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Yue, Xiaowei
中科院分区：
文献类型：
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作者：
Wang, Yinan;Sun, Wenbo;Jin, Jionghua;Kong, Zhenyu;Yue, Xiaowei
表面缺陷识别是许多制造系统中的一项重要任务，包括汽车、飞机、轧钢和预制混凝土。虽然已经提出了基于图像的表面缺陷识别方法，但这些方法通常有两个局限性：图像可能会丢失表面缺陷的深度等部分信息，并且其精度容易受到检查角度、光线、颜色、噪声等多种因素的影响。鉴于三维点云能够准确地表示表面缺陷的多维结构，本文的目标是利用三维点云对表面缺陷进行检测和分类。这有两大挑战：(1)缺陷往往稀疏地分布在曲面上，这使得它们的特征容易被法线曲面隐藏；(2)三维点云的不同排列和变换可能代表同一曲面，因此所提出的模型需要具有排列和变换不变性。为了研究三维点云数据中的缺陷模式，提出了一种两步法表面缺陷识别方法。该方法包括缺陷检测的无监督方法和缺陷分类的多视点深度学习模型，该模型能够跟踪缺陷区域和非缺陷区域的特征。我们证明了该方法对不同的排列和变换是不变的。分别对合成飞机机身和实际预制混凝土试件进行了表面缺陷识别的两个案例研究。结果表明，与其他基准方法相比，我们的方法获得了最好的缺陷检测和分类准确率。
Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.