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相关概念视频

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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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.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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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...
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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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相关实验视频

Updated: Jul 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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提高无人机视觉着陆识别与YOLO的物体检测通过机载边缘计算.

Ming-You Ma1, Shang-En Shen1, Yi-Cheng Huang1

  • 1Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan.

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

这项研究提高了无人机 (UAV) 的视觉能力,使用TensorRT加速的You Only Look Once (YOLO) 对象检测. 该系统在实时登陆和侦察任务中实现了高FPS.

科学领域:

  • 机器人和计算机视觉 机器人和计算机视觉
  • 航空航天工程 航空航天工程

背景情况:

  • 无人机 (UAV) 需要有效的机载物体检测,用于导航和侦察.
  • 当前系统在实时处理,数据传输和在不同环境中的准确性方面面临着挑战.

研究的目的:

  • 提高无人机在着陆和侦察任务中的视觉能力.
  • 使用边缘计算来提高对象检测速度和准确性.
  • 为了减少地面站的数据传输和处理时间.

主要方法:

  • 在内置边缘计算机上使用TensorRT加速实现基于You Only Look Once (YOLO) 的对象检测.
  • 使用自动视觉跟踪杆摄像机控制系统.
  • 采用多线程编程来实现高效的图像传输.
  • 将四个YOLO模型进行比较,并将YOLOv4-tiny应用于现实世界的实地测试.

主要成果:

  • 在无人机上通过TensorRT加速的YOLO实现了每秒高率 (FPS).
  • 通过轻量级边缘计算证明了令人满意的平均平均精度 (mAP).
  • 成功地应用训练有素的YOLOv4微型模型来识别100公里外的未知环境中的着陆点.
  • 证实了使用NVIDIA Jetson Xavier NX与YOLO实现超过35 FPS的可行性.
关键词:
无人机无人机无人机是什么?这是一个YOLO YOLO.对象检测检测对象检测对象检测

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结论:

  • 拟议的方法显著提高无人机实时物体检测和视觉能力.
  • 该系统在新的环境中展示了成功的自主着陆和侦察潜力.
  • 整合YOLO,TensorRT和边缘计算为先进的无人机任务提供了可行的解决方案.