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Metal surface defect detection based on improved YOLOv5
During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model...
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/PMC10681978/ https://www.ncbi.nlm.nih.gov/pubmed/38012224 http://dx.doi.org/10.1038/s41598-023-47716-2 |
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author | Zhou, Chuande Lu, Zhenyu Lv, Zhongliang Meng, Minghui Tan, Yonghu Xia, Kewen Liu, Kang Zuo, Hailun |
author_facet | Zhou, Chuande Lu, Zhenyu Lv, Zhongliang Meng, Minghui Tan, Yonghu Xia, Kewen Liu, Kang Zuo, Hailun |
author_sort | Zhou, Chuande |
collection | PubMed |
description | During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed. |
format | Online Article Text |
id | pubmed-10681978 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106819782023-11-30 Metal surface defect detection based on improved YOLOv5 Zhou, Chuande Lu, Zhenyu Lv, Zhongliang Meng, Minghui Tan, Yonghu Xia, Kewen Liu, Kang Zuo, Hailun Sci Rep Article During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed. Nature Publishing Group UK 2023-11-27 /pmc/articles/PMC10681978/ /pubmed/38012224 http://dx.doi.org/10.1038/s41598-023-47716-2 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 Zhou, Chuande Lu, Zhenyu Lv, Zhongliang Meng, Minghui Tan, Yonghu Xia, Kewen Liu, Kang Zuo, Hailun Metal surface defect detection based on improved YOLOv5 |
title | Metal surface defect detection based on improved YOLOv5 |
title_full | Metal surface defect detection based on improved YOLOv5 |
title_fullStr | Metal surface defect detection based on improved YOLOv5 |
title_full_unstemmed | Metal surface defect detection based on improved YOLOv5 |
title_short | Metal surface defect detection based on improved YOLOv5 |
title_sort | metal surface defect detection based on improved yolov5 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10681978/ https://www.ncbi.nlm.nih.gov/pubmed/38012224 http://dx.doi.org/10.1038/s41598-023-47716-2 |
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