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相关概念视频

Vision01:24

Vision

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.
Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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相关实验视频

Updated: May 8, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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基于多视觉的内部线程缺陷检测系统.

Xiaohan Dou1, Chengqi Xue1, Gengpei Zhang1

  • 1Yangtze University, Jingzhou, Hubei, China.

PloS one
|May 28, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于高精度内部线程检查的自动化机器视觉系统. 它提高了检测速度和准确性,为狭小空间提供了高效的AI解决方案.

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科学领域:

  • 工业工程 工业工程 工业工程
  • 计算机视觉 计算机视觉
  • 计量学 计量学 计量学

背景情况:

  • 评估内线质量对于机械完整性至关重要,但由于空间,照明和几何复杂性而具有挑战性.
  • 传统的手动检查方法在内部线程评估方面耗时且效率低下.
  • 现有的自动化技术在图像采集和复杂线程纹理的拼接方面遇到了困难.

研究的目的:

  • 开发一个使用机器视觉的自动化内部线程检测方案.
  • 提高工业环境中内部线程检查的速度和准确性.
  • 提供一个高效的AI解决方案,用于在狭窄的空间中检测几何体的表面缺陷.

主要方法:

  • 优化照明和图像捕捉设备,以更快地获取内部线程图像.
  • 开发了一种内部线程图像拼接技术,利用圆柱形模型进行全视线程成像.
  • 使用YOLOv8模型在线程图像中精确定位缺陷.

主要成果:

  • 与现有技术相比,显著提高了内部线程图像采集的速度.
  • 通过使用圆柱形模型合复杂的线程纹理,成功生成全视线程图像.
  • 通过YOLOv8模型实现,通过缺陷检测实现了更高的准确性和效率.

结论:

  • 拟议的自动化系统为内部线程检查提供了一个高效和直观的人工智能解决方案.
  • 优化成像,圆柱形和YOLOv8检测的整合解决了在狭小空间中检查几何体的关键挑战.
  • 这项研究促进了关键机械元件的工业质量控制中的机器视觉应用.