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Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting

机译：基于3D点云通过多种结构配件的飞机皮铆检测

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摘要

Rivet detection is usually the first step for almost all surface and rivet inspection methods in aircraft skins. With 3D laser scanners, one can rapidly obtain the precise 3D information, i.e. point cloud, of the surface and rivets. Subsequently, rivet detection can be converted to a multiple-structure fitting problem from 3D point clouds. However, robust structure fitting from scanned 3D point cloud remains an open problem due to its challenging nature, such as noise and outliers, irregular sampling density and missing scanning. To reduce the fitting variability, this paper presents an automated density-aware multiple-structure fitting algorithm to perform rivet detection based on a 3D point cloud. The key observation is that the local density of points belonging to the rivet contour is relatively higher. We hereby formulate rivet detection as a multiple structure fitting problem with a density-based significance measure. By considering the local distribution characteristics, we first perform adaptive density enhancement on the basic local density. Subsequently, we detect the potential circle hypotheses and thereby extract rivet contours. By performing the mode-seeking algorithm on hypergraphs, all the circle structures can be obtained simultaneously. Overall, the proposed extraction algorithm is able to efficiently and effectively detect rivets from the raw scanned point clouds. We also demonstrate that the proposed algorithm achieves significant superiority over several state-of-the-art model fitting methods on the real scanned point cloud via experimental results. Moreover, we give the application of our algorithm on rivet flush inspection, showing that our method can assist in the rapid measurement of riveting quality. (C) 2019 Elsevier Ltd. All rights reserved.
机译：铆钉检测通常是飞机皮肤中几乎所有表面和铆钉检查方法的第一步。通过3D激光扫描仪，可以迅速获得精确的3D信息，即点云，表面和铆钉。随后，可以将铆钉检测转换为来自3D点云的多结构拟合问题。然而，由于其具有挑战性的性质，例如噪声和异常值，不规则采样密度和缺失扫描，因此扫描3D点云的鲁棒结构仍然是一个打开的问题。为了降低拟合变化，本文提出了一种自动密度感知的多结构拟合算法，用于基于3D点云执行铆钉检测。关键观察是属于铆钉轮廓的局部密度相对较高。我们在此引入铆钉检测作为基于密度的显着性测量的多结构拟合问题。通过考虑局部分布特性，我们首先对基本局部密度进行自适应密度增强。随后，我们检测潜在的圆假设，从而提取铆钉轮廓。通过在超图上执行寻找模式算法，可以同时获得所有圆结构。总的来说，所提取的提取算法能够有效地和有效地检测来自原始扫描点云的铆钉。我们还证明，通过实验结果，所提出的算法在真正的扫描点云上实现了几种最先进的模型拟合方法的显着优势。此外，我们提供了我们在铆钉冲洗检查上的应用，表明我们的方法可以帮助快速测量铆接质量。 （c）2019年elestvier有限公司保留所有权利。

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