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Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition

Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from...

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Detalles Bibliográficos
Autores principales: Chung, Yi, Chou, Chih-Ang, Li, Chih-Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7908595/
https://www.ncbi.nlm.nih.gov/pubmed/33499249
http://dx.doi.org/10.3390/ijerph18030961
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author Chung, Yi
Chou, Chih-Ang
Li, Chih-Yang
author_facet Chung, Yi
Chou, Chih-Ang
Li, Chih-Yang
author_sort Chung, Yi
collection PubMed
description Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from the requirements of an in-field scenario. First, we propose a central attention concept that helps focus on the target instead of backgrounds in the image for tree species recognition. It could prevent model training from confused vision by establishing a dual path CNN deep learning framework, in which the central attention model combined with the CNN model based on InceptionV3 were employed to automatically extract the features. These two models were then learned together with a shared classification layer. Experimental results assessed the effectiveness of our proposed approach which outperformed each uni-path alone, and existing methods in the whole plant recognition system. Additionally, we created our own tree image database where each photo contained a wealth of information on the entire tree instead of an individual plant organ. Lastly, we developed a prototype system of an online/offline available tree species identification working on a consumer mobile platform that can identify the tree species not only by image recognition, but also detection and classification in real-time remotely.
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spelling pubmed-79085952021-02-27 Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition Chung, Yi Chou, Chih-Ang Li, Chih-Yang Int J Environ Res Public Health Article Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from the requirements of an in-field scenario. First, we propose a central attention concept that helps focus on the target instead of backgrounds in the image for tree species recognition. It could prevent model training from confused vision by establishing a dual path CNN deep learning framework, in which the central attention model combined with the CNN model based on InceptionV3 were employed to automatically extract the features. These two models were then learned together with a shared classification layer. Experimental results assessed the effectiveness of our proposed approach which outperformed each uni-path alone, and existing methods in the whole plant recognition system. Additionally, we created our own tree image database where each photo contained a wealth of information on the entire tree instead of an individual plant organ. Lastly, we developed a prototype system of an online/offline available tree species identification working on a consumer mobile platform that can identify the tree species not only by image recognition, but also detection and classification in real-time remotely. MDPI 2021-01-22 2021-02 /pmc/articles/PMC7908595/ /pubmed/33499249 http://dx.doi.org/10.3390/ijerph18030961 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chung, Yi
Chou, Chih-Ang
Li, Chih-Yang
Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition
title Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition
title_full Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition
title_fullStr Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition
title_full_unstemmed Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition
title_short Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition
title_sort central attention and a dual path convolutional neural network in real-world tree species recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7908595/
https://www.ncbi.nlm.nih.gov/pubmed/33499249
http://dx.doi.org/10.3390/ijerph18030961
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