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相关概念视频

Distance Measurements by Taping01:18

Distance Measurements by Taping

Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short distances...
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...

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Updated: Jun 16, 2026

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SDC-DeepLabv3+:轻量级和精确的定位算法用于树收获机器人

Zhenyu Xing1,2, Zhenguo Zhang1,3, Yunze Wang1

  • 1College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.

Plant phenomics (Washington, D.C.)
|July 8, 2024
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概括
此摘要是机器生成的。

这项研究引入了一种改进的DeepLabv3+算法,用于准确地检测和定位红杉收获机器人中的丝采摘点. 这种新方法通过克服小,众多和模糊的细丝所面临的挑战来增强机器人收获.

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科学领域:

  • 农业机器人农业机器人
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 机器人收获面临着小,众多和模糊的细丝的挑战,阻碍了准确的表型提取.
  • 由于近色背景和模糊轮,局部化的困难限制了光纤采集中的机器人精度.

研究的目的:

  • 开发一个改进的DeepLabv3+算法,用于精确检测和定位丝采集点.
  • 提高机器人收获系统的准确性和效率,用于复杂的纤维结构的作物.

主要方法:

  • 一个改进的DeepLabv3+算法,使用ShuffletNetV2作为轻量级的骨干.
  • 纳入具有不同采样速率的卷积分支和卷积块的注意力,以增强特征提取.
  • 开发基于 barycenter 投影的导线采集点定位算法,使用改进的 DeepLabv3+ 的感兴趣区域.

主要成果:

  • 改进的DeepLabv3+实现了高精度,平均像素精度为95.84%和平均交叉度超过联合率为96.87%.
  • 与其他方法相比,该算法显示出更高的检测速率和更小的重量文件大小.
  • 成功定位和采摘率平均分别为92.50%和90.83%,视觉定位误差最小化.

结论:

  • 拟议的方法为机器人系统中的丝采集本地化提供了一种可行且准确的方法.
  • 增强的算法有效地解决了与光线可见性和背景干扰相关的挑战.
  • 这一进步有助于更精确,更高效的农业自动化收获.