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相关概念视频

Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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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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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...
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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Updated: Jan 9, 2026

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深度学习框架用于使用模拟无人机图像进行条形码本地化和解码.

Faris Alsulami1, N Z Jhanjhi2,3

  • 1Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, 23890, Jeddah, Saudi Arabia. fnalsulami@uj.edu.sa.

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概括
此摘要是机器生成的。

本研究介绍了一种使用无人机 (UAV) 扫描条码的自动仓库库存系统. 深度学习框架在检测条形码方面实现了高精度,提高了物流效率.

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科学领域:

  • 物流和供应链管理的物流和供应链管理.
  • 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

背景情况:

  • 自动仓库库存跟踪对于物流效率和减少错误至关重要.
  • 无人驾驶飞行器 (UAV) 为自动空中条形码扫描提供了一个可行的解决方案.
  • 实时条形码检测面临着诸如照明不良,阴影和遮蔽等挑战.

研究的目的:

  • 开发和评估深度学习框架,用于使用模拟无人机图像进行自动化的条形码库存管理.
  • 在复杂的仓库环境中提高条形码检测和解码的可靠性.
  • 为了演示一个无人机准备系统,用于现实世界的无人机部署.

主要方法:

  • 利用YOLOv8对象检测模型从无人机角度准确地定位1D和2D条形码.
  • 使用OpenCV的条形码模块来解码本地化的条形码区域.
  • 将提取的数据集成到MySQL数据库中,用于模拟实时库存更新.

主要成果:

  • 在条码检测方面获得了92.4%的平均平均精度 (mAP),这表明在具有挑战性的条件下表现强.
  • 从模拟的无人机图像中成功定位和解码条形码.
  • 展示了一个从图像捕获到数据库更新的功能管道.

结论:

  • 拟议的深度学习框架显示了通过无人机可靠的自动化仓库库存管理的巨大潜力.
  • 该系统的模块化设计使无人机准备部署在最小的调整.
  • 这种方法可以大大减少在库存跟踪过程中的人力资源和错误.