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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-7908595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chungyi centralattentionandadualpathconvolutionalneuralnetworkinrealworldtreespeciesrecognition AT chouchihang centralattentionandadualpathconvolutionalneuralnetworkinrealworldtreespeciesrecognition AT lichihyang centralattentionandadualpathconvolutionalneuralnetworkinrealworldtreespeciesrecognition |