点云
计算机科学
分割
人工智能
规范化(社会学)
交叉熵
深度学习
编码器
交叉口(航空)
数据挖掘
模式识别(心理学)
工程类
人类学
操作系统
社会学
航空航天工程
作者
Ankang Ji,Limao Zhang,Hongqin Fan,Xiaolong Xue,Yudan Dou
标识
DOI:10.1016/j.autcon.2023.105131
摘要
Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep learning network named dual attention-based point cloud network (DAPCNet) is developed in this paper to act on point clouds for segmentation. In the developed model, data normalization and feature aggregation are first processed to eliminate data discrepancies and enhance local features, after which the processed data are input into the built network layers based on the encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. Furthermore, a custom loss function called Facal Cross-Entropy ("FacalCE") is designed to enhance the model's ability to extract and learn features while addressing imbalanced data distribution. To validate the effectiveness and feasibility of the developed model, a dataset of tunnel point clouds collected from a real engineering project in China is employed. The experimental results indicate that (1) the developed model has excellent performance with Mean Intersection over Union (MIoU) of 0.8597, (2) the improved 3D dual attention module and "FacalCE" contribute to the model performance, respectively, and (3) the developed model is superior to other state-of-the-art methods, such as PointNet and DGCNN. In summary, the DAPCNet model exhibits exceptional performance, offering effective and accurate results for segmenting multi-class objects within tunnel point clouds.
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