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YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit
Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex...
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/PMC9218684/ https://www.ncbi.nlm.nih.gov/pubmed/35755692 http://dx.doi.org/10.3389/fpls.2022.765523 |
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author | Jia, Weikuan Liu, Mengyuan Luo, Rong Wang, Chongjing Pan, Ningning Yang, Xinbo Ge, Xinting |
author_facet | Jia, Weikuan Liu, Mengyuan Luo, Rong Wang, Chongjing Pan, Ningning Yang, Xinbo Ge, Xinting |
author_sort | Jia, Weikuan |
collection | PubMed |
description | Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex orchard backgrounds. For the green fruit segmentation, an efficient YOLOF-snake segmentation model is proposed. First, the ResNet101 structure is adopted as the backbone network to achieve feature extraction of the green object fruit. Then, the C5 feature maps are expanded with receptive fields and the decoder is used for classification and regression. Besides, the center point in the regression box is employed to get a diamond-shaped structure and fed into an additional Deep-snake network, which is adjusted to the contours of the target fruit to achieve fast and accurate segmentation of green fruit. The experimental results show that YOLOF-snake is sensitive to the green fruit, and the segmentation accuracy and efficiency are significantly improved. The proposed model can effectively extend the application of agricultural equipment and provide theoretical references for other fruits and vegetable segmentation. |
format | Online Article Text |
id | pubmed-9218684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92186842022-06-24 YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit Jia, Weikuan Liu, Mengyuan Luo, Rong Wang, Chongjing Pan, Ningning Yang, Xinbo Ge, Xinting Front Plant Sci Plant Science Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex orchard backgrounds. For the green fruit segmentation, an efficient YOLOF-snake segmentation model is proposed. First, the ResNet101 structure is adopted as the backbone network to achieve feature extraction of the green object fruit. Then, the C5 feature maps are expanded with receptive fields and the decoder is used for classification and regression. Besides, the center point in the regression box is employed to get a diamond-shaped structure and fed into an additional Deep-snake network, which is adjusted to the contours of the target fruit to achieve fast and accurate segmentation of green fruit. The experimental results show that YOLOF-snake is sensitive to the green fruit, and the segmentation accuracy and efficiency are significantly improved. The proposed model can effectively extend the application of agricultural equipment and provide theoretical references for other fruits and vegetable segmentation. Frontiers Media S.A. 2022-06-09 /pmc/articles/PMC9218684/ /pubmed/35755692 http://dx.doi.org/10.3389/fpls.2022.765523 Text en Copyright © 2022 Jia, Liu, Luo, Wang, Pan, Yang and Ge. 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 Jia, Weikuan Liu, Mengyuan Luo, Rong Wang, Chongjing Pan, Ningning Yang, Xinbo Ge, Xinting YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit |
title | YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit |
title_full | YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit |
title_fullStr | YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit |
title_full_unstemmed | YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit |
title_short | YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit |
title_sort | yolof-snake: an efficient segmentation model for green object fruit |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9218684/ https://www.ncbi.nlm.nih.gov/pubmed/35755692 http://dx.doi.org/10.3389/fpls.2022.765523 |
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