Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation-Reference-Cited by-同舟云学术

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Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation

Published:2024-02 Issue: Volume:158 Page:105214
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Liang Jia,Zhang Qipeng,Gu Xingyu

Funder

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

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

Reference56 articles.

1. A survey on monochrome image segmentation methods;Sridevi;Procedia Technol.,2012

2. Modeling automatic pavement crack object detection and pixel-level segmentation;Du;Autom. Constr.,2023

3. Multi-scale triple-attention network for pixelwise crack segmentation;Yang;Autom. Constr.,2023

4. Optimization and sensitivity analysis of existing deep learning models for pavement surface monitoring using low-quality images;Roberts;Autom. Constr.,2022

5. Semantic segmentation of cracks: data challenges and architecture;Panella;Autom. Constr.,2022

Cited by 5 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. Deployment strategies for lightweight pavement defect detection using deep learning and inverse perspective mapping;Automation in Construction;2024-11

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

4. CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation;Automation in Construction;2024-08

5. Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model;Earth Science Informatics;2024-05-28

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