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An accurate green fruits detection method based on optimized YOLOX-m
Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order t...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200941/ https://www.ncbi.nlm.nih.gov/pubmed/37223802 http://dx.doi.org/10.3389/fpls.2023.1187734 |
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author | Jia, Weikuan Xu, Ying Lu, Yuqi Yin, Xiang Pan, Ningning Jiang, Ru Ge, Xinting |
author_facet | Jia, Weikuan Xu, Ying Lu, Yuqi Yin, Xiang Pan, Ningning Jiang, Ru Ge, Xinting |
author_sort | Jia, Weikuan |
collection | PubMed |
description | Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network’s ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables. |
format | Online Article Text |
id | pubmed-10200941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102009412023-05-23 An accurate green fruits detection method based on optimized YOLOX-m Jia, Weikuan Xu, Ying Lu, Yuqi Yin, Xiang Pan, Ningning Jiang, Ru Ge, Xinting Front Plant Sci Plant Science Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network’s ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables. Frontiers Media S.A. 2023-05-08 /pmc/articles/PMC10200941/ /pubmed/37223802 http://dx.doi.org/10.3389/fpls.2023.1187734 Text en Copyright © 2023 Jia, Xu, Lu, Yin, Pan, Jiang 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 Xu, Ying Lu, Yuqi Yin, Xiang Pan, Ningning Jiang, Ru Ge, Xinting An accurate green fruits detection method based on optimized YOLOX-m |
title | An accurate green fruits detection method based on optimized YOLOX-m |
title_full | An accurate green fruits detection method based on optimized YOLOX-m |
title_fullStr | An accurate green fruits detection method based on optimized YOLOX-m |
title_full_unstemmed | An accurate green fruits detection method based on optimized YOLOX-m |
title_short | An accurate green fruits detection method based on optimized YOLOX-m |
title_sort | accurate green fruits detection method based on optimized yolox-m |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200941/ https://www.ncbi.nlm.nih.gov/pubmed/37223802 http://dx.doi.org/10.3389/fpls.2023.1187734 |
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