Machine vision

Abstract and Figures

Understanding machine vision and its applications

Machine vision systems are usually defined as "technology that allows a computing device to inspect, evaluate, and identify still or moving images." One of the most basic ways to understand a machine vision system is to think of it as the "eyes" of the machine. The system determines action based on digital input acquired by a camera. Machine vision systems are used by businesses to improve quality, efficiency, and operations in many ways.

When employed in manufacturing, a machine vision system consists of an array of electrical components, computer hardware, and software that work together to interpret and analyze collected pictures for operational guidance. The following are the typical components:

  • Sensors
  • Frame-grabber
  • Digital and analog cameras
  • Sufficient lighting for cameras to capture high-quality imagery
  • Images that can be analyzed using software and computers
  • Pattern recognition algorithms (useful in multiple fields)
  • Output devices like a screen or a set of mechanical components

To mention a few applications, the technology and techniques may be utilized to discover faults, offer product sorting, barcode scanning, end-of-line vehicle inspection, product checking, and robotic manufacturing.

Machine vision can also be a powerful image-processing tool, allowing for the recognition and classification of objects, patterns, and other data. Stitching, filtering, thresholding, pixel counting, segmentation, edge detection, color analysis, and blob identification are all steps in this procedure. Deep learning and machine learning processing have greatly expanded machine vision capabilities, yielding classification, pattern recognition, barcode scanning, optical character recognition, and measuring methodology. Machine vision systems can usually measure things like:

  • Pass or fail decisions
  • Object position and orientation information
  • Numerical measurement data
  • Data read from codes and characters
  • Object counts (like paper boxes, bottles or other forms of packaging) and classification
  • Process or result displays which are stored for later use

Deep learning has enabled the creation of image-based robot guidance, which provides location and orientation information to let robots grip products and move.
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