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Fruit classification using attention-based MobileNetV2 for industrial applications
Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necess...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880666/ https://www.ncbi.nlm.nih.gov/pubmed/35213643 http://dx.doi.org/10.1371/journal.pone.0264586 |
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author | Shahi, Tej Bahadur Sitaula, Chiranjibi Neupane, Arjun Guo, William |
author_facet | Shahi, Tej Bahadur Sitaula, Chiranjibi Neupane, Arjun Guo, William |
author_sort | Shahi, Tej Bahadur |
collection | PubMed |
description | Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future. |
format | Online Article Text |
id | pubmed-8880666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88806662022-02-26 Fruit classification using attention-based MobileNetV2 for industrial applications Shahi, Tej Bahadur Sitaula, Chiranjibi Neupane, Arjun Guo, William PLoS One Research Article Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future. Public Library of Science 2022-02-25 /pmc/articles/PMC8880666/ /pubmed/35213643 http://dx.doi.org/10.1371/journal.pone.0264586 Text en © 2022 Shahi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Shahi, Tej Bahadur Sitaula, Chiranjibi Neupane, Arjun Guo, William Fruit classification using attention-based MobileNetV2 for industrial applications |
title | Fruit classification using attention-based MobileNetV2 for industrial applications |
title_full | Fruit classification using attention-based MobileNetV2 for industrial applications |
title_fullStr | Fruit classification using attention-based MobileNetV2 for industrial applications |
title_full_unstemmed | Fruit classification using attention-based MobileNetV2 for industrial applications |
title_short | Fruit classification using attention-based MobileNetV2 for industrial applications |
title_sort | fruit classification using attention-based mobilenetv2 for industrial applications |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880666/ https://www.ncbi.nlm.nih.gov/pubmed/35213643 http://dx.doi.org/10.1371/journal.pone.0264586 |
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