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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...

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Autores principales: Shahi, Tej Bahadur, Sitaula, Chiranjibi, Neupane, Arjun, Guo, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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.
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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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