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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

122
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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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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相关实验视频

Updated: Jul 26, 2025

Fruit Volatile Analysis Using an Electronic Nose
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在葡萄园中基于同步检测算法进行非结构化的道路提取和路边果实识别.

Xinzhao Zhou1,2, Xiangjun Zou2,3, Wei Tang2

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.

Frontiers in plant science
|June 19, 2023
PubMed
概括

一个新的算法准确地提取道路,并在复杂的果园中识别路边葡萄. 这提高了机器人的水果采摘和导航感知,提高了检测能力23.84%和速度14.33%.

关键词:
深度学习是一种深度学习.收获水果的机器人收获水果机器视觉 机器视觉 机器视觉非结构性环境的非结构性环境.路边水果检测 检测路边水果检测

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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:

  • 准确的道路提取和路边水果识别对于自主农业机器人至关重要.
  • 复杂的果园环境由于非结构化的地形和视觉干扰而带来了重大挑战.
  • 现有的方法经常在现实条件下与道路检测和水果识别同时作斗争.

研究的目的:

  • 开发一种新的算法,同时进行非结构化的道路提取和果园的路边水果识别.
  • 提高农业机器人的感知能力,用于果和导航等任务.
  • 在复杂和非结构化的现场环境中解决当前方法的局限性.

主要方法:

  • 一种预处理方法,涉及感兴趣区域拦截,双边过,对数空间转换和基于MSRCR的图像增强.
  • 一种双空间融合道路提取方法,利用颜色通道增强和优化的灰色系数分析.
  • 一个优化的YOLOv7模型,用于增强随机分布的葡萄集群的识别,集成到一个融合框架与道路提取结果.

主要成果:

  • 拟议的预加工方法有效地减少了不利环境因素的干扰,提高了道路开采质量.
  • 优化的YOLOv7模型在路边水果集群检测方面取得了很高的性能 (精度:88.9%,召回:89.7%,mAP:93.4%,F1得分:89.3%),表现优于YOLOv5.
  • 与独立检测算法相比,同步算法提高了水果识别的23.84%和检测速度的14.33%.

结论:

  • 开发的算法成功实现了在复杂的果园环境中同步的道路提取和路边果实检测.
  • 优化的YOLOv7模型在野外条件下表现出优越的葡萄识别性能.
  • 这项研究为增强农业机器人的感知和决策系统提供了坚实的基础.