CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation-Reference-Cited by-同舟云学术

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CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation

Published:2024-08 Issue: Volume:164 Page:105482
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Liang Jia,Gu Xingyu,Jiang Dong,Zhang Qipeng

Funder

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Reference68 articles.

1. Transportation Industry Development Statistical Bulletin;Ministry of Transport of the People's Republic of China,2022

2. Commercial Management and Financing of Roads;Heggie,1998

3. An Asian Perspective;Road Funds and Road Maintenance,2003

4. Fully convolutional networks for semantic segmentation;Shelhamer;IEEE Trans. Pattern Anal. Mach. Intell.,2017

5. Encoder-decoder with atrous separable convolution for semantic image segmentation;Chen,2024

Cited by 2 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. Small-sample data-driven lightweight convolutional neural network for asphalt pavement defect identification;Case Studies in Construction Materials;2024-12

2. Enhancing pavement crack segmentation via semantic diffusion synthesis model for strategic road assessment;Results in Engineering;2024-09

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