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Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision
Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increa...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383684/ https://www.ncbi.nlm.nih.gov/pubmed/37514870 http://dx.doi.org/10.3390/s23146576 |
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author | Xie, Shuang Sun, Hongwei |
author_facet | Xie, Shuang Sun, Hongwei |
author_sort | Xie, Shuang |
collection | PubMed |
description | Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model’s ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model’s parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment. |
format | Online Article Text |
id | pubmed-10383684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103836842023-07-30 Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision Xie, Shuang Sun, Hongwei Sensors (Basel) Article Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model’s ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model’s parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment. MDPI 2023-07-21 /pmc/articles/PMC10383684/ /pubmed/37514870 http://dx.doi.org/10.3390/s23146576 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xie, Shuang Sun, Hongwei Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision |
title | Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision |
title_full | Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision |
title_fullStr | Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision |
title_full_unstemmed | Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision |
title_short | Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision |
title_sort | tea-yolov8s: a tea bud detection model based on deep learning and computer vision |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383684/ https://www.ncbi.nlm.nih.gov/pubmed/37514870 http://dx.doi.org/10.3390/s23146576 |
work_keys_str_mv | AT xieshuang teayolov8sateabuddetectionmodelbasedondeeplearningandcomputervision AT sunhongwei teayolov8sateabuddetectionmodelbasedondeeplearningandcomputervision |