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Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing
A high resolution dataset is one of the prerequisites for tea chrysanthemum detection with deep learning algorithms. This is crucial for further developing a selective chrysanthemum harvesting robot. However, generating high resolution datasets of the tea chrysanthemum with complex unstructured envi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021924/ https://www.ncbi.nlm.nih.gov/pubmed/35463441 http://dx.doi.org/10.3389/fpls.2022.850606 |
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author | Qi, Chao Gao, Junfeng Chen, Kunjie Shu, Lei Pearson, Simon |
author_facet | Qi, Chao Gao, Junfeng Chen, Kunjie Shu, Lei Pearson, Simon |
author_sort | Qi, Chao |
collection | PubMed |
description | A high resolution dataset is one of the prerequisites for tea chrysanthemum detection with deep learning algorithms. This is crucial for further developing a selective chrysanthemum harvesting robot. However, generating high resolution datasets of the tea chrysanthemum with complex unstructured environments is a challenge. In this context, we propose a novel tea chrysanthemum – generative adversarial network (TC-GAN) that attempts to deal with this challenge. First, we designed a non-linear mapping network for untangling the features of the underlying code. Then, a customized regularization method was used to provide fine-grained control over the image details. Finally, a gradient diversion design with multi-scale feature extraction capability was adopted to optimize the training process. The proposed TC-GAN was compared with 12 state-of-the-art generative adversarial networks, showing that an optimal average precision (AP) of 90.09% was achieved with the generated images (512 × 512) on the developed TC-YOLO object detection model under the NVIDIA Tesla P100 GPU environment. Moreover, the detection model was deployed into the embedded NVIDIA Jetson TX2 platform with 0.1 s inference time, and this edge computing device could be further developed into a perception system for selective chrysanthemum picking robots in the future. |
format | Online Article Text |
id | pubmed-9021924 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90219242022-04-22 Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing Qi, Chao Gao, Junfeng Chen, Kunjie Shu, Lei Pearson, Simon Front Plant Sci Plant Science A high resolution dataset is one of the prerequisites for tea chrysanthemum detection with deep learning algorithms. This is crucial for further developing a selective chrysanthemum harvesting robot. However, generating high resolution datasets of the tea chrysanthemum with complex unstructured environments is a challenge. In this context, we propose a novel tea chrysanthemum – generative adversarial network (TC-GAN) that attempts to deal with this challenge. First, we designed a non-linear mapping network for untangling the features of the underlying code. Then, a customized regularization method was used to provide fine-grained control over the image details. Finally, a gradient diversion design with multi-scale feature extraction capability was adopted to optimize the training process. The proposed TC-GAN was compared with 12 state-of-the-art generative adversarial networks, showing that an optimal average precision (AP) of 90.09% was achieved with the generated images (512 × 512) on the developed TC-YOLO object detection model under the NVIDIA Tesla P100 GPU environment. Moreover, the detection model was deployed into the embedded NVIDIA Jetson TX2 platform with 0.1 s inference time, and this edge computing device could be further developed into a perception system for selective chrysanthemum picking robots in the future. Frontiers Media S.A. 2022-04-07 /pmc/articles/PMC9021924/ /pubmed/35463441 http://dx.doi.org/10.3389/fpls.2022.850606 Text en Copyright © 2022 Qi, Gao, Chen, Shu and Pearson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Qi, Chao Gao, Junfeng Chen, Kunjie Shu, Lei Pearson, Simon Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing |
title | Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing |
title_full | Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing |
title_fullStr | Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing |
title_full_unstemmed | Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing |
title_short | Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing |
title_sort | tea chrysanthemum detection by leveraging generative adversarial networks and edge computing |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021924/ https://www.ncbi.nlm.nih.gov/pubmed/35463441 http://dx.doi.org/10.3389/fpls.2022.850606 |
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