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相关概念视频

Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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Flying Insect Detection and Classification with Inexpensive Sensors
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基于深度学习的果计数用于收益预测,使用拟议的飞行机器人系统.

Şahin Yıldırım1, Burak Ulu1

  • 1Department of Mechatronic Engineering, Erciyes University, Kayseri 38039, Turkey.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

这项研究表明,深度学习模型,特别是Faster Region-CNN (Faster R-CNN) 和单拍多盒检测 (SSD) Mobilenet,可以使用自定义数据集准确地检测和计算果. 更快的R-CNN模型实现了高精度,有助于精确预测果生产者的产量.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 农业技术 农业技术

背景情况:

  • 卷积神经网络 (CNN) 深度学习在基于视觉特征的水果检测和分类方面普遍存在.
  • 准确的水果产量预测对于生产者建立商业协议至关重要.

研究的目的:

  • 评估两个深度学习模型的性能,即单击多盒检测 (SSD) Mobilenet和更快的区域-CNN (更快的R-CNN),用于自主果检测和计数.
  • 为了比较这些模型的有效性,当训练在一个定制数据集与预先训练的COCO数据集.
  • 通过自动化果园监测,为果种植者提供准确的产量预测.

主要方法:

  • 创建了4000个红果图像的自定义数据集,用于培训.
  • 使用自定义数据集训练SSD-Mobilenet和Faster R-CNN模型,其学习率在0.015-0.04.之间.
  • 开发了一种飞行机器人系统 (FRS),用于在果园中自动检测和计数果.
  • 实验性地比较训练模型的性能.

主要成果:

  • 无论是SSD-Mobilenet还是Faster R-CNN模型,都实现了高达93%的准确率.
  • 更快的R-CNN模型表现出非常成功的检测性能,将损失值降低到0.1.
关键词:
空中机器人机器人技术农业自动化农业自动化计算机视觉技术的使用.深度学习是一种深度学习.对象检测检测对象检测对象检测

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  • 自动检测和计数在一个商业果园中成功实施.

    • 结论:

    • 更快的R-CNN和SSD-Mobilenet是有效的深度学习架构,用于自主果检测和产量估计.
    • 使用定制数据集的培训显著提高了特定果品种的模型性能.
    • 使用这些模型的自动化系统可以为农业生产者提供有价值的工具,以改善产量预测.