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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-10313685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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