To address the inaccurate classification of coarse aggregate particle size due to insufficient height information in single‐view, a multi‐view and graph convolutional network (GCN) based method for coarse aggregate particle size classification was proposed in this study. First, the viewpoint selection and projection strategies were designed to build the aggregate multi‐view datasets. Then, the surface texture of the aggregate was reconstructed by using 3D point cloud information. Finally, self‐attention mechanism and three‐layer GCN were introduced to aggregate global shape feature descriptors. The experimental results show that the proposed interleaved self‐attention and view GCN model achieves a coarse aggregate particle size classification accuracy of 94.11%, outperforming other multi‐view classification algorithms. This method provides a new possibility for the accurate detection of aggregate particle size and provides significant support for the production and automatic detection of aggregate raw materials.

中文翻译：

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一种融合 3D 多视图和图卷积网络的粗骨料粒径分类方法


针对单视图高度信息不足导致粗骨料粒径分类不准确的问题，本研究提出了一种基于多视图和图卷积网络 （GCN） 的粗骨料粒径分类方法。首先，设计视点选择和投影策略来构建聚合多视图数据集。然后，利用三维点云信息重建骨料的表面纹理;最后，引入自注意力机制和三层 GCN 来聚合全局形状特征描述符。实验结果表明，所提出的交错式自注意力和视图 GCN 模型实现了 94.11% 的粗集合体粒径分类准确率，优于其他多视图分类算法。该方法为骨料粒度的准确检测提供了新的可能性，为骨料原料的生产和自动检测提供了重要支持。