Written by

Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

View Latest Book

What is Machine Vision And How Is It Used In Business Today?

2 July 2021

One of the simplest ways to understand a machine vision system is to consider it the “eyes” of a machine. The system uses digital input that’s captured by a camera to determine action. Businesses use machine vision systems in a variety of ways to improve quality, efficiency and operations.

How do machine vision systems work?

Some manufacturing facilities have used machine vision systems since the 1950s, but it was in the 1980s-1990s when things really started to expand. Regardless of an industrial or non-industrial application, a combination of software and hardware work together to make machine vision systems possible. Here are the typical components involved:

  • Sensors
  • Frame-grabber
  • Cameras (digital or analogue)
  • Lighting sufficient for cameras to capture quality images
  • Software and computer capable of analysing images
  • Algorithms that can identify patterns; important in some use cases
  • Output such as a screen or mechanical components

Let’s look at how these components work together when machine vision is used to inspect a product in a manufacturing operation, a very common example of a machine vision system in practice.

The process begins when a sensor detects the presence of a product. The sensor then triggers a light source to illuminate the area and a camera to capture an image of the product or a component of the product. The frame-grabber (a digitising device) translates the camera’s image into digital output. The digital file is saved on a computer so it can be analysed by the system software. The software compares the file against a set of predetermined criteria to identify defects. If a defect is identified, the product will fail inspection.

What’s the difference between machine vision and computer vision?

Computer vision and machine vision are overlapping technologies. A machine vision system requires a computer and specific software to operate while computer vision doesn’t need to be integrated with a machine. Computer vision can, for example, analyse digital online images or videos as well as “images” from motion detectors, infrared sensors or other sources, not just a photo or video. Machine vision is a sub-category of computer vision.

How is machine vision used in business?

In addition to using machine vision for quality control purposes, it is helping businesses in many ways today for identification, inspection, guidance and more. Here are a few examples:

Correcting production line defects: In addition to using machine vision to identify defective products, machine vision can help determine where the problems are being introduced in a production line so corrective action can be taken.

Farming: Machine vision is used by harvesting machines to detect the location of grapes on the vine so that robotic harvesting machines can pick the bunches without destroying any grapes. Machine vision is also used as part of farm machinery to monitor crops and detect diseases on plants.

Inventory control and management: Machine vision is imperative in the process of reading barcodes and labels on components and products. This has important applications for inventory control, but also in the manufacturing process to ensure the correct components get added as products move down an assembly line. Machine vision is critical for the bin-picking done in warehouses by robots.

Product tracking and traceability: In heavily regulated industries such as pharmaceuticals, it’s important to be able to track ingredients, product serial numbers and monitor expiration dates which machine vision makes extraordinarily easier.

Measurements and calibration: Whether measuring the gap in a spark plug to ensure it fits specifications or identifying a gauge that needs calibrated, machine vision automates and makes the process quite efficient.

Safety: Whether on a construction site with heavy equipment or tracking food supplies, machine vision can improve safety with great efficiency.

As the technology continues to get more sophisticated, the use cases for machine vision will continue to grow.

Data Strategy Book | Bernard Marr

Related Articles

How Do We Use Artificial Intelligence Ethically | Bernard Marr

How Do We Use Artificial Intelligence Ethically?

I’m hugely passionate about artificial intelligence (AI), and I'm proud to say that I help companies use AI to do amazing things in the world [...]

How Artificial Intelligence Can Help Small Businesses | Bernard Marr

How Artificial Intelligence Can Help Small Businesses

Small and medium-sized businesses all over the world are benefiting from artificial intelligence and machine learning – and integrating AI into core business functions and processes is getting more accessible and more affordable every day. [...]

What Really Is The Tesla Bot And How Much Will It Cost | Bernard Marr

What Really Is The Tesla Bot And How Much Will It Cost?

Elon Musk has just announced that Tesla will begin developing a humanoid robot called the Tesla Bot that is designed to perform “unsafe, repetitive, or boring” tasks. [...]

Should I Choose Machine Learning or Big Data | Bernard Marr

Should I Choose Machine Learning or Big Data?

Big Data and Machine Learning are two exciting applications of technology that are often mentioned together in the space of the same breath [...]

What Is The Next Level Of AI Technology | Bernard Marr

What Is The Next Level Of AI Technology?

Artificial Intelligence (AI) has permeated all aspects of our lives – from the way we communicate to how we work, shop, play, and do business. [...]

The 7 Biggest Ethical Challenges of Artificial Intelligence | Bernard Marr

The 7 Biggest Ethical Challenges of Artificial Intelligence

Today, artificial intelligence is essential across a wide range of industries, including healthcare, retail, manufacturing, and even government. [...]

Stay up-to-date

  • Get updates straight to your inbox
  • Join my 1 million newsletter subscribers
  • Never miss any new content

Social Media



View Podcasts