Building crack identification and total quality management method based on deep learning-Reference-Cited by-同舟云学术

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Building crack identification and total quality management method based on deep learning

Published:2021-05 Issue: Volume:145 Page:225-231
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Wu Xinhua,Liu Xiujie

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference25 articles.

1. Eggshell crack identification based on welch power spectrum and generalized regression neural network(GRNN);Tianhua;Food Sci.,2015

2. Crack detection study for hydraulic concrete using PPP-BOTDA;Huang;Smart Struct. Syst.,2017

3. A real time nondestructive crack detection system for the automotive stamping process;Song;IEEE Trans. Instrument. Meas.,2016

4. IR Reflectance imaging for crystalline Si solar cell crack detection;Brooks;IEEE J. Photovoltaics,2015

5. Elasticity nonlinear diagnostic method for crack detection and depth estimation;Bas;J. Acoust. Soc. Am.,2015

Cited by 27 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. A non-local based microcrack segmentation model optimized for effective high resolution and low-power devices;Journal of Building Engineering;2024-11

2. Meta-heuristic algorithms: an appropriate approach in crack detection;Innovative Infrastructure Solutions;2024-06-22

3. CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation;Entropy;2024-04-12

4. ConSE: An ontology for visual representation and semantic enrichment of digital images in construction sites;Advanced Engineering Informatics;2024-04

5. Deepcrack: A Deep Learning Approach for Image-Based Crack Prediction using MobileNet And Transfer Learning;2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT);2024-01-04

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