CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework-Reference-Cited by-同舟云学术

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CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework

Published:2022-02-18 Issue:6 Volume:16 Page:782-799
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET Intelligent Trans Sys

Author:

Yuan Genji¹^ORCID,Li Jianbo¹²,Meng Xianglong¹,Li Yinong¹

Affiliation:

1. College of Computer Science and Technology Qingdao University Qingdao China

2. Institute of Ubiquitous Networks and Urban Computing Qingdao University Qingdao China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12173

Reference70 articles.

1. CrackW‐Net: A novel pavement crack image segmentation convolutional neural network;Han C.;IEEE Trans. Intell. Transp. Syst.,2021

2. Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

3. Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-Taken Images

4. Mixed pooling and richer attention feature fusion for crack detection

5. Multi‐scale classification network for road crack detection

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1. Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism;Applied Sciences;2024-09-10

2. Automatic crack segmentation model based on multi-branch aggregation transformer;Advances in Structural Engineering;2024-07-23

3. Road crack detection using pixel classification and intensity-based distinctive fuzzy C-means clustering;The Visual Computer;2024-06-22

4. CrackTinyNet: A novel deep learning model specifically designed for superior performance in tiny road surface crack detection;IET Intelligent Transport Systems;2024-02-27

5. Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers;Applied System Innovation;2024-01-22

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