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Visual System01:26

Visual System

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Related Experiment Video

Updated: May 29, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

INSPECTOR: A Computer Vision System that Learns to Inspect Parts.

W A Perkins1

  • 1Computer Science Department, General Motors Research Laboratories, Warren, MI 49090; Lockheed Palo Alto Research Laboratory, Palo Alto, CA 94304.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

A new computer vision system learns to identify good and bad industrial parts by analyzing examples. This automated inspection technology accurately detects defects and classifies different part types.

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Related Experiment Videos

Last Updated: May 29, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Automated quality control is crucial in manufacturing.
  • Traditional inspection methods can be time-consuming and prone to human error.
  • Developing intelligent systems for defect detection is an ongoing challenge.

Purpose of the Study:

  • To develop a computer vision system for automated part inspection.
  • To enable the system to learn and distinguish between acceptable and defective parts.
  • To create a flexible system capable of handling various defect types and part classifications.

Main Methods:

  • The system utilizes a machine learning approach, training on a dataset of identified good and bad parts.
  • A model is generated during training, incorporating identifying points for part localization and region-specific inspection tests.
  • The trained model is then applied to new parts for automated assessment.

Main Results:

  • The computer vision system successfully learns to differentiate between good and bad parts.
  • The system accurately identifies parts with an arbitrary number of defects.
  • The developed model demonstrates the capability to classify different types of parts based on training data.

Conclusions:

  • The developed computer vision inspection system offers an effective solution for automated quality control.
  • The system's ability to learn from examples and adapt to different parts enhances its industrial applicability.
  • This technology has the potential to improve efficiency and accuracy in manufacturing inspection processes.