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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

527
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.
527

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相关实验视频

Updated: May 30, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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基于双眼立体视觉的相对定位算法,用于无人机群.

Qing Cheng1, Yazhe Wang2

  • 1School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, 618300, China.

Scientific reports
|January 27, 2025
PubMed
概括

本研究介绍了一种使用Yolo-SGN的轻型无人机 (UAV) 定位算法,用于增强实时双筒视觉形成飞行. 这种新的方法显著减少了计算,提高了无人机导航的精度和速度.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 航空航天工程 航空航天工程

背景情况:

  • 基于双筒视觉的无人机 (UAV) 形成飞行面临着高计算复杂性和低实时性能的挑战.
  • 现有的方法往往难以平衡动态无人机操作的准确性和效率.

研究的目的:

  • 开发一个高效和精确的无人机定位算法,用于编队飞行.
  • 改进无人机中的双眼视觉系统的实时性能.

主要方法:

  • 使用轻量级设计原则优化YOLOv5s模型,创建Yolo-SGN.
  • 使用Yolo-SGN用于对双眼图像的目标特征点提取,结合面向快速和旋转简要 (ORB) 算法.
  • 实施双眼视觉定位模型,使用提取的特征点来计算无人机定位的3D坐标.

主要成果:

  • 与原来的YOLOv5s相比,Yolo-SGN模型实现了65.5%的参数减少,FLOP减少了62.7%,精度增加了1.8%.
  • 功能点匹配计算减少到原始ORB算法的负载的四分之一.
  • 拟议的算法在无人机定位方面表现出了卓越的精度和实时能力.

结论:

关键词:
双眼双眼立体视觉视觉深度学习是一种深度学习.轻量级网络轻量级的网络.无人驾驶飞行器检测系统 无人驾驶飞行器检测系统

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  • 轻量级的Yolo-SGN模型显著提高了无人机应用中对象检测的效率.
  • 通过将Yolo-SGN与ORB和双眼视觉定位集成,为实时无人机飞行提供了强大的解决方案.
  • 这种方法为提高自主无人机系统的性能和适用性提供了一个有希望的方向.