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Related Concept Videos

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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Internal thread defect detection system based on multi-vision.

Xiaohan Dou1, Chengqi Xue1, Gengpei Zhang1

  • 1Yangtze University, Jingzhou, Hubei, China.

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|May 28, 2024
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Summary
This summary is machine-generated.

This study introduces an automated machine vision system for high-precision internal thread inspection. It enhances detection speed and accuracy, offering an efficient AI solution for confined spaces.

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Area of Science:

  • Industrial Engineering
  • Computer Vision
  • Metrology

Background:

  • Assessing internal thread quality is vital for mechanical integrity but challenging due to space, lighting, and geometric complexities.
  • Traditional manual inspection methods are time-consuming and inefficient for internal thread assessment.
  • Existing automated technologies face difficulties with image acquisition and stitching for complex thread textures.

Purpose of the Study:

  • To develop an automated internal thread detection scheme using machine vision.
  • To improve the speed and accuracy of internal thread inspection in industrial settings.
  • To provide an efficient AI solution for detecting surface defects on geometric bodies in confined spaces.

Main Methods:

  • Optimized lighting and image capturing devices for faster internal thread image acquisition.
  • Developed an internal thread image stitching technique utilizing a cylindrical model for full-view thread imaging.
  • Employed the YOLOv8 model for precise defect localization within thread images.

Main Results:

  • Significantly improved the speed of internal thread image acquisition compared to existing technologies.
  • Successfully generated full-view thread images by stitching complex thread textures using the cylindrical model.
  • Achieved enhanced accuracy and efficiency in defect detection through YOLOv8 model implementation.

Conclusions:

  • The proposed automated system offers an efficient and intuitive artificial intelligence solution for internal thread inspection.
  • The integration of optimized imaging, cylindrical stitching, and YOLOv8 detection addresses key challenges in inspecting geometric bodies in confined spaces.
  • This research advances machine vision applications in industrial quality control for critical mechanical components.