Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds-Reference-Cited by-同舟云学术

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

Published:2023-12 Issue: Volume:156 Page:105131
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Ji Ankang,Zhang Limao,Fan Hongqin,Xue Xiaolong,Dou Yudan

Funder

National Natural Science Foundation of China

Hong Kong Polytechnic University

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference64 articles.

1. Robust metro train scheduling integrated with skip-stop pattern and passenger flow control strategy under uncertain passenger demands;Hu;Comput. Oper. Res.,2023

2. Metro systems and urban development: impacts and implications;Lin;Tunn. Undergr. Sp. Technol.,2022

3. Multispectral water leakage detection based on a one-stage anchor-free modality fusion network for metro tunnels;Han;Autom. Constr.,2022

4. Automatic defect detection of metro tunnel surfaces using a vision-based inspection system;Li;Adv. Eng. Inform.,2021

5. Fragility of transport assets exposed to multiple hazards: state-of-the-art review toward infrastructural resilience;Argyroudis;Reliab. Eng. Syst. Saf.,2019

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improved building MEP systems semantic segmentation in point clouds using a novel multi-class dataset and local–global vector transformer network;Journal of Building Engineering;2024-11

2. A two-step deep learning-based framework for metro tunnel lining defect recognition;Tunnelling and Underground Space Technology;2024-08

3. Multi-scale learnable key-channel attention network for point cloud classification and segmentation;Applied Soft Computing;2024-07

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