A crack-segmentation algorithm fusing transformers and convolutional neural networks for complex detection scenarios
Author:
Publisher
Elsevier BV
Subject
Building and Construction,Civil and Structural Engineering,Control and Systems Engineering
Reference61 articles.
1. An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement;Ji;Autom. Constr.,2020
2. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning;Kang;Autom. Constr.,2020
3. Binocular video-based 3d reconstruction and length quantification of cracks in concrete structures;Deng;Autom. Constr.,2023
4. Structural crack detection using deep convolutional neural networks;Ali;Autom. Constr.,2022
5. Automated crack segmentation in close-range building façade inspection images using deep learning techniques;Chen;J. Build. Eng.,2021
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