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Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat im...

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Detalles Bibliográficos
Autores principales: Wang, Yiding, Qin, Yuxin, Cui, Jiali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8226325/
https://www.ncbi.nlm.nih.gov/pubmed/34177976
http://dx.doi.org/10.3389/fpls.2021.645899
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author Wang, Yiding
Qin, Yuxin
Cui, Jiali
author_facet Wang, Yiding
Qin, Yuxin
Cui, Jiali
author_sort Wang, Yiding
collection PubMed
description Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods.
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spelling pubmed-82263252021-06-26 Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning Wang, Yiding Qin, Yuxin Cui, Jiali Front Plant Sci Plant Science Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods. Frontiers Media S.A. 2021-06-11 /pmc/articles/PMC8226325/ /pubmed/34177976 http://dx.doi.org/10.3389/fpls.2021.645899 Text en Copyright © 2021 Wang, Qin and Cui. 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
Wang, Yiding
Qin, Yuxin
Cui, Jiali
Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning
title Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning
title_full Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning
title_fullStr Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning
title_full_unstemmed Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning
title_short Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning
title_sort occlusion robust wheat ear counting algorithm based on deep learning
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8226325/
https://www.ncbi.nlm.nih.gov/pubmed/34177976
http://dx.doi.org/10.3389/fpls.2021.645899
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