Extraction and Recognition of Components from Point Clouds of Industrial Plants
Extraction and Recognition of Components from Point Clouds of Industrial Plants
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DOI：
10.14733/cadconfp.2020.111-115
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发表时间：
2020-05
影响因子：
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通讯作者：
Kohei Shigeta;H. Masuda
中科院分区：
文献类型：
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作者：
Kohei Shigeta;H. Masuda
.工业厂房的点云对于支持改造规划、生产和产品设计、资产管理等非常有用。然而，工业厂房的点云包含大量组件。为了利用点云，有必要从点云中提取每个组件并识别其类型。在本文中，我们讨论了使用机器学习识别工业工厂中的组件类型的方法。在我们的方法中，圆柱和平面检测点云和候选组件区域提取。由于使用地面激光扫描仪捕获的点云可以映射到2D网格上，因此可以应用为图像设计的卷积神经网络（CNN）。从点云生成三种类型的2D图像，并将它们用于分类。为了增加训练数据的数量，使用CAD模型来增强深度图像。在评估中，对九个分类器进行了训练和评估。通过比较九种CNN模型，我们讨论了适用于识别工业厂房中组件的分类器。
. Point clouds of industrial plants are very useful for supporting renovation planning, production and product design, asset management, and so on. However, point clouds of an industrial plant contain a large number of components. In order to utilize point clouds, it is necessary to extract each component from point clouds and identify its type. In this paper, we discuss methods for identifying component types in industrial plants using machine learning. In our method, cylinders and planes are detected from point-clouds and candidate component regions are extracted. Since point clouds captured using the terrestrial laser scanner can be mapped on the 2D grid, convolutional neural network (CNN) designed for images can be applied. Three types of 2D images are generated from point clouds, and they are used for classification. To increase the numbers of training data, depth images are augmented using CAD models. In evaluation, nine classifiers were trained and evaluated. By comparing the nine CNN models, we discuss classifiers suitable for recognizing components in industrial plants.