MobileNetV3-BLS: A broad learning approach for automatic concrete surface crack detection-Reference-Cited by-同舟云学术

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MobileNetV3-BLS: A broad learning approach for automatic concrete surface crack detection

Published:2023-08 Issue: Volume:392 Page:131941
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Zhang Jing,Cai Yuan-Yuan,Yang Dong,Yuan Ye,He Wen-Yu,Wang Yan-Jia

Funder

Ministry of Science and Technology of the People's Republic of China

Natural Science Foundation for Distinguished Young Scholars of Anhui Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference58 articles.

1. Fast and robust pavement crack distress segmentation utilizing steerable filtering and local order energy;Liang;Constr. Build. Mater.,2020

2. Asphalt pavement pothole detection and segmentation based on wavelet energy field;Wang;Math. Probl. Eng.,2017

3. A novel study for the estimation of crack propagation in concrete using machine learning algorithms;Bayar;Constr. Build. Mater.,2019

4. Crack detection using image processing: A critical review and analysis;Mohan;Alex. Eng. J.,2018

5. Concrete crack detection and monitoring using a capacitive dense sensor array;Yan;Sensors,2019

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2. MFAFNet: An innovative crack intelligent segmentation method based on multi-layer feature association fusion network;Advanced Engineering Informatics;2024-10

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4. An automated measurement method for the fatigue crack propagation based on decorrelated digital image correlation;International Journal of Fatigue;2024-06

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