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ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism

The proportion of natural sand replaced by steel slag sand affects the volumetric stability of steel slag mortar and steel slag concrete. However, the steel slag substitution rate detection method is inefficient and lacks representative sampling. Therefore, a deep learning-based steel slag sand subs...

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Autores principales: Teng, Shengjie, Zhu, Lin, Li, Yunze, Wang, Xinnian, Jin, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313685/
https://www.ncbi.nlm.nih.gov/pubmed/37391568
http://dx.doi.org/10.1038/s41598-023-37676-y
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author Teng, Shengjie
Zhu, Lin
Li, Yunze
Wang, Xinnian
Jin, Qiang
author_facet Teng, Shengjie
Zhu, Lin
Li, Yunze
Wang, Xinnian
Jin, Qiang
author_sort Teng, Shengjie
collection PubMed
description The proportion of natural sand replaced by steel slag sand affects the volumetric stability of steel slag mortar and steel slag concrete. However, the steel slag substitution rate detection method is inefficient and lacks representative sampling. Therefore, a deep learning-based steel slag sand substitution rate detection method is proposed. The technique adds a squeeze and excitation (SE) attention mechanism to the ConvNeXt model to improve the model's efficiency in extracting the color features of steel slag sand mix. Meanwhile, the model's accuracy is further enhanced by using the migration learning method. The experimental results show that SE can effectively help ConvNeXt acquire images' color features. The model's accuracy in predicting the replacement rate of steel slag sand is 87.99%, which is better than the original ConvNeXt network and other standard convolutional neural networks. After using the migration learning training method, the model predicts the steel slag sand substitution rate with 92.64% accuracy, improving accuracy by 4.65%. The SE attention mechanism and the migration learning training method can help the model acquire the critical features of the image better and effectively improve the model's accuracy. The method proposed in this paper can identify the steel slag sand substitution rate quickly and accurately and can be used for the detection of the steel slag sand substitution rate.
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spelling pubmed-103136852023-07-02 ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism Teng, Shengjie Zhu, Lin Li, Yunze Wang, Xinnian Jin, Qiang Sci Rep Article The proportion of natural sand replaced by steel slag sand affects the volumetric stability of steel slag mortar and steel slag concrete. However, the steel slag substitution rate detection method is inefficient and lacks representative sampling. Therefore, a deep learning-based steel slag sand substitution rate detection method is proposed. The technique adds a squeeze and excitation (SE) attention mechanism to the ConvNeXt model to improve the model's efficiency in extracting the color features of steel slag sand mix. Meanwhile, the model's accuracy is further enhanced by using the migration learning method. The experimental results show that SE can effectively help ConvNeXt acquire images' color features. The model's accuracy in predicting the replacement rate of steel slag sand is 87.99%, which is better than the original ConvNeXt network and other standard convolutional neural networks. After using the migration learning training method, the model predicts the steel slag sand substitution rate with 92.64% accuracy, improving accuracy by 4.65%. The SE attention mechanism and the migration learning training method can help the model acquire the critical features of the image better and effectively improve the model's accuracy. The method proposed in this paper can identify the steel slag sand substitution rate quickly and accurately and can be used for the detection of the steel slag sand substitution rate. Nature Publishing Group UK 2023-06-30 /pmc/articles/PMC10313685/ /pubmed/37391568 http://dx.doi.org/10.1038/s41598-023-37676-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Teng, Shengjie
Zhu, Lin
Li, Yunze
Wang, Xinnian
Jin, Qiang
ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism
title ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism
title_full ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism
title_fullStr ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism
title_full_unstemmed ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism
title_short ConvNeXt steel slag sand substitution rate detection method incorporating attention mechanism
title_sort convnext steel slag sand substitution rate detection method incorporating attention mechanism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313685/
https://www.ncbi.nlm.nih.gov/pubmed/37391568
http://dx.doi.org/10.1038/s41598-023-37676-y
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