Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds

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 f...

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Vydáno v:Automation in construction Ročník 156; s. 105131
Hlavní autoři: Ji, Ankang, Zhang, Limao, Fan, Hongqin, Xue, Xiaolong, Dou, Yudan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.12.2023
Témata:
3D dual attention module
Deep learning
Encoder-decoder
Semantic segmentation
Tunnel point cloud
Deep learning
Tunnel point cloud
3D dual attention module
Semantic segmentation
Encoder-decoder
ISSN:0926-5805, 1872-7891
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Shrnutí: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. •A deep learning method named DAPCNet is developed for 3D point cloud segmentation.•An improved 3D dual attention module is introduced to enhance model performance.•A custom loss “FacalCE” is designed to strengthen feature learning with handling data imbalance.•Conducting comparisons to examine the model performance for segmentation.•The developed method demonstrates outstanding performance, achieving an MIoU score of 0.8597 when applied to tunnel point clouds.
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2023.105131