Machine Vision: Definition, Setup and Applications

What is Machine Vision?

Machine vision is a set of technologies that enables machines to see, analyze, and act in industrial environments. It involves visual systems that can perform tasks like quality control, object detection, part measurement, and barcode reading. While computer vision is a broader scientific discipline, machine vision focuses on practical, integrated technological solutions for specific industrial applications.

Machine Vision Setup

A typical machine vision setup combines specialized hardware and software to capture and analyze visual information. 

Cameras

Industrial cameras form the critical visual input mechanism of machine vision systems. These specialized cameras range from high-resolution sensors capturing microscopic details to advanced optical systems designed for specific environmental conditions. They can detect wavelengths and visual nuances far beyond human-eye capabilities, providing precise and consistent visual data across many different applications.

Lighting

Specialized lighting systems (including structured LED illumination, diffused light sources, and wavelength-specific lighting) create optimal imaging environments. Proper lighting ensures consistent image quality, enhances contrast, and enables accurate visual interpretation by minimizing shadows, reflections, and environmental variations.

Computer Power

Machine vision systems require computer power to process and analyze visual data. There are three primary deployment models.

Cloud-based processing

• Ability to scale resources according to demand
• Centralized data management
• High-latency solution

Edge device processing

• Real-time local analysis
• Low-latency performance
• Exceptional control over resources and data
• Scenarios where connectivity to the cloud is insufficient

Hybrid processing architectures

• Combines local and cloud computing
• Provides a balanced approach that optimizes performance, security, and scalability
• Good for organizations that need real-time processing capabilities in an edge environment
• Leverages the expansive computational power of the cloud for large-scale data analysis and storage

Software

Machine vision software has two main categories: the traditional rule-based software and the newer AI-based vision software.

Traditional Machine Vision

Traditional machine vision software operates on predefined rules and specific inspection criteria. These systems use threshold-driven analysis to evaluate visual data against established parameters, providing deterministic and predictable outcomes. Condition-based software excels in structured environments with clear, unchanging inspection requirements, such as measuring parts, checking if a bottle is full, or checking for empty spaces in packaging.

AI-Based Machine Vision

Modern machine vision leverages artificial intelligence through advanced deep learning algorithms and neural networks. These intelligent systems can adapt, learn, and improve their visual recognition capabilities over time. AI-based software enables more complex pattern recognition, handles variability in visual data, and supports autonomous decision-making across increasingly sophisticated applications. Complex defect detection, surface imperfections and soldering flaws are some examples.

2D vs 3D Machine Vision

2D

2D machine vision looks at flat surfaces, capturing images from a single angle. It's fast and works well for simple checks like finding surface defects or reading labels on products.

3D

3D machine vision adds depth to visual analysis, seeing objects from multiple angles. This approach helps measure complex shapes, understand object volumes, and recognize detailed geometric features that 2D systems might miss.

Machine Vision Applications