Evaluating Angularity of Coarse Aggregates Using the Virtual Cutting Method Based on 3D Point Cloud Images
Evaluating Angularity of Coarse Aggregates Using the Virtual Cutting Method Based on 3D Point Cloud Images
复制标题
基于 3D 点云图像的虚拟切割方法评估粗骨料的角度
DOI：
10.1109/access.2020.3013901
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Xueli Hao
中科院分区：
文献类型：
--
作者：
Hanye Liu;Zhaoyun Sun;Wei Li;Ju Huyan;Meng Guo;Xueli Hao
本文提出了一种新的方法，称为虚拟切割方法来评估三维点云粗骨料图像的棱角性指数（AI）值，其目的是表征输送带上骨料的棱角性。粗骨料的三维点云图像首先被捕获，预处理，并分割成单个的三维骨料对象。基于处理后的三维集合体图像，利用相邻两个平面之间具有相等角度的一系列相交平面来提取相交轮廓。通过使用AIMS 2系统中使用的梯度法平均轮廓的倾斜度来评价AI。然后进行统计分析以选择两个相邻平面之间的最佳角度。结果发现，五度角是理想的角度，因为它可以平衡该方法的执行时间和有效性。最后，将虚拟切割法的人工智能结果与二维、三维投影法的人工智能结果进行了比较。结果发现，三种方法对不同骨料纹理的AI排名基本一致。本研究的结果表明，虚拟切割方法可以用来量化的棱角性的一个单一的集料或集料堆在输送带上的基础上的三维点云图像。
In this paper, a new method called the Virtual Cutting Method is proposed to evaluate the angularity index (AI) values of 3D point cloud coarse aggregate images with the aim of characterizing the angularity of aggregates on conveyor belts. The 3D point cloud images of coarse aggregates were first captured, preprocessed, and segmented into single 3D aggregate objects. Based on the processed 3D aggregate images, intersection contours were extracted using a series of intersection planes with an equivalent angle between two adjacent planes. The AI was evaluated by averaging the angularity of the contours using the gradient method, which was used in the AIMS2 system. Statistical analysis was then performed to select the optimum angle between two adjacent planes. It was found that an angle of five degrees was the ideal angle, as it can balance the execution time and effectiveness of the method. Finally, the AI results of the Virtual Cutting Method were compared with those of 2D and 3D Projection Methods. It was found that the AI rankings of the three methods for different aggregate textures are generally consistent. The findings of this study conclude that the Virtual Cutting Method can be employed to quantify the angularity of a single aggregate or aggregates in piles on conveyor belts based on 3D point cloud images.