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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
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相关实验视频

Updated: Jan 10, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于U-Net的果园导航线识别方法的研究

Ning Xu1,2,3, Xiangsen Ning4, Aijuan Li4

  • 1College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的U-Net模型,用于果园导航线识别,提高可驱动区域细分的准确性. 该方法有效地提取导航线,为复杂的果园环境中的视觉自主导航提供可靠的解决方案.

关键词:
在U-Net网络中,U-Net是U-Net网络.注意力机制注意力机制可分割的驾驶区域.导航线是指导航线的导航线.果园环境 果园环境 果园环境

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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:

  • 果园视觉导航面临来自复杂背景和干扰的挑战.
  • 现有的方法在准确的可驾驶区域细分和导航线提取方面扎.

研究的目的:

  • 开发一种基于U-Net的增强方法,用于强大的果园导航线识别.
  • 提高视觉导航系统在农业环境中的准确性和可靠性.

主要方法:

  • 通过空间注意力 (SA) 和坐标注意力 (CA) 机制改进了U-Net语义细分模型.
  • 创建了一个果园数据集,并用于训练可驱动区域细分的增强模型.
  • 导航线从细分面具中提取,使用几何中心点计算和斜线插值.

主要成果:

  • 改进后的模型实现了高细分精度,回忆率为90.23%,精度为91.71%,mPA为87.75%,mIoU为84.84%.
  • 与实际的中心线相比,提取的导航线的平均距离误差为56毫米.
  • 性能与U-Net,SegViT,SE-Net和DeepLabv3+进行了验证.

结论:

  • 拟议的基于U-Net的方法显著改善了可驾驶区域的细分和果园中的导航线的提取.
  • 这种方法为具有挑战性的农业环境中的视觉自主导航系统提供了有效的参考.