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A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation
Industrial quality detection is one of the important fields in machine vision. Big data analysis, the Internet of Things, edge computing, and other technologies are widely used in industrial quality detection. Studying an industrial detection algorithm that can be organically combined with the Inter...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576371/ https://www.ncbi.nlm.nih.gov/pubmed/36262617 http://dx.doi.org/10.1155/2022/6174255 |
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author | Zhang, Ze-Kai Zhou, Ming-Le Shao, Rui Li, Min Li, Gang |
author_facet | Zhang, Ze-Kai Zhou, Ming-Le Shao, Rui Li, Min Li, Gang |
author_sort | Zhang, Ze-Kai |
collection | PubMed |
description | Industrial quality detection is one of the important fields in machine vision. Big data analysis, the Internet of Things, edge computing, and other technologies are widely used in industrial quality detection. Studying an industrial detection algorithm that can be organically combined with the Internet of Things and edge computing is imminent. Deep learning methods in industrial quality detection have been widely proposed recently. However, due to the particularity of industrial scenarios, the existing deep learning-based general object detection methods have shortcomings in industrial applications. This study designs two isomorphic industrial detection models to solve these problems: T-model and S-model. Both proposed models combine swin-transformer with convolution in the backbone and design a residual fusion path. In the neck, this study designs a dual attention module to improve feature fusion. Second, this study presents a knowledge distiller based on the dual attention module to improve the detection accuracy of the lightweight S-model. According to the analysis of the experimental results on four public industrial defect detection datasets, the model in this study is more advantageous in industrial defect detection. |
format | Online Article Text |
id | pubmed-9576371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95763712022-10-18 A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation Zhang, Ze-Kai Zhou, Ming-Le Shao, Rui Li, Min Li, Gang Comput Intell Neurosci Research Article Industrial quality detection is one of the important fields in machine vision. Big data analysis, the Internet of Things, edge computing, and other technologies are widely used in industrial quality detection. Studying an industrial detection algorithm that can be organically combined with the Internet of Things and edge computing is imminent. Deep learning methods in industrial quality detection have been widely proposed recently. However, due to the particularity of industrial scenarios, the existing deep learning-based general object detection methods have shortcomings in industrial applications. This study designs two isomorphic industrial detection models to solve these problems: T-model and S-model. Both proposed models combine swin-transformer with convolution in the backbone and design a residual fusion path. In the neck, this study designs a dual attention module to improve feature fusion. Second, this study presents a knowledge distiller based on the dual attention module to improve the detection accuracy of the lightweight S-model. According to the analysis of the experimental results on four public industrial defect detection datasets, the model in this study is more advantageous in industrial defect detection. Hindawi 2022-10-10 /pmc/articles/PMC9576371/ /pubmed/36262617 http://dx.doi.org/10.1155/2022/6174255 Text en Copyright © 2022 Ze-Kai Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Ze-Kai Zhou, Ming-Le Shao, Rui Li, Min Li, Gang A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation |
title | A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation |
title_full | A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation |
title_fullStr | A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation |
title_full_unstemmed | A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation |
title_short | A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation |
title_sort | defect detection model for industrial products based on attention and knowledge distillation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576371/ https://www.ncbi.nlm.nih.gov/pubmed/36262617 http://dx.doi.org/10.1155/2022/6174255 |
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