Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Coordination-Driven Metal-Phenolic Network Complexes for Programmable Colorimetric Signal Transduction and Defined Nano-Bio Interfaces.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Corrigendum to "Associations between multiple metals exposure and cognitive function in the middle-aged and older adults from China: A cross-sectional study" [Environ. Res., 263(Pt 1), 2024, 120038].

Environmental research·2026

Same author

SPP1 promotes lymph node metastasis and malignant progression of head and neck squamous cell carcinoma by activating FAK-AKT signaling pathway.

World journal of surgical oncology·2026

Same author

A retrospective cohort study comparing the effects on inflammatory response and clinical outcomes of vancomycin-PMMA versus vancomycin-sulfate calcium in chronic osteomyelitis.

Annals of medicine·2026

Same author

Author Correction: Parishin A-loaded mesoporous silica nanoparticles modulate macrophage polarization to attenuate tendinopathy.

NPJ Regenerative medicine·2026

Same author

Nanozymes-Armed Probiotic <i>Lactobacillus plantarum</i> Coordinates Ferroptosis-like Bacterial Death for Diabetic Wound Therapy.

Research (Washington, D.C.)·2026

Search research articles

相关实验视频

Updated: Jun 30, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

小物体检测算法包括茶叶芽的swin变压器.

Meiling Shi¹, Dongling Zheng¹, Tianhao Wu²

¹College of Information Engineering, Sichuan Agricultural University, Ya'an, China.

|March 21, 2024

概括

精确的茶叶芽识别用于机器人收获是通过STF-YOLO算法改进的,它增强了特征提取,以获得更好的茶叶质量和产量. 这种新的方法在复杂环境中显著提高了小物体检测性能.

科学领域:

计算机视觉计算机视觉
机器人技术机器人技术机器人技术
农业技术农业技术

背景情况:

精确识别小茶芽对于自动茶叶收获机器人至关重要,影响茶叶质量和产量.
现有的图像处理和机器学习方法与茶叶的复杂性和多样性作斗争,导致精度和强度较低.
小茶蕾的微妙特征和形态对当前的检测算法构成重大挑战.

研究的目的:

提出一种新的小物体检测算法,STF-YOLO (用Swin变压器和聚焦YOLO检测小目标),用于准确识别小茶叶芽.
在复杂的环境中增强小物体的特征表示和检测能力.
为了提高茶叶芽识别的准确性和稳定性,用于机器人收获应用.

主要方法:

集成Swin变压器模块用于使用自我注意力机制的先进视觉特征提取.
利用YOLOv8网络架构,结合Focus和Depthwise Convolution模块,以提高计算效率和改进功能处理.
利用智能交叉对联盟损失函数进行网络优化.
在自创的茶蕾数据集上进行开发和实验.

主要成果:

在茶蕾数据集上,STF-YOLO模型实现了91.5%的精度和89.4%的平均平均精度 (mAP).

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

相关实验视频

Last Updated: Jun 30, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

与主流算法 (YOLOv8,YOLOv7,YOLOv5,YOLOx) 相比显著改进,准确度提高了5-20.22个百分点,F1得分提高了0.03-0.13.

拟议的算法在提高复杂农业环境中小物体检测性能方面被证明是有效的.

结论:

STF-YOLO为识别小茶叶芽提供了强大而准确的解决方案,解决了现有方法的局限性.
这项研究为推进自动茶叶收获提供了必要的技术手段,并提供了对小物体检测挑战的见解.
未来的工作可以专注于进一步的模型优化,数据增强和模型融合,以提高各种场景的概括性和稳定性.