Computer Vision Vs. Human Inspection: Catch More Defects and Cut Costs with Automated Quality Control

Computer vision systems identify defects more accurately than people do, which can have a big impact on quality control and revenue. Manufacturers should explore robotic solutions with built-in computer vision.

May 23rd, 2023 4 min read

Visual inspection is vital to quality control at multiple stages in production, whether you need to check discrete parts before they are assembled, make sure each step in an assembly is performed correctly, pull defective products off the line or examine finished goods for surface defects.

The vast majority of this work — as much as 80% according to some estimates — is performed by human inspectors.

Here’s the problem: human inspectors miss between 20% and 30% of defects

Why is this the case? Because traditional visual inspection is extremely demanding. On a typical production line, an inspector may be looking at hundreds or thousands of parts, trying to spot very small cracks, scratches or similar surface-level imperfections. Humans simply aren’t equipped to maintain a high level of visual and mental concentration over long periods of time. Visual inspection is also subjective, so two inspectors could examine the same part and reach different conclusions.

Is the status quo hurting your bottom line?

If a defective product finds its way to your customer, the implications are serious.

According to the American Society for Quality (ASQ), quality-related costs range from 15 to 20% of sales to as much as 40% of total operations. They add that even for a “thriving” company, poor quality costs up to 15% of operations. That’s a lot of money left on the table for any manufacturer.

Revenue loss is just one consequence, too. Manufacturers serving OEMs will lose bids if they become known for poor product quality. Plus, in industries where quality is tied directly to human life — such as medical devices, automotive and aerospace — the stakes are even higher.

So what’s the alternative to human inspection?

Explore the advantages of computer vision

Computer vision systems use multiple cameras to “see” objects in three dimensions and understand their shape, location and orientation within well-defined parameters. These systems can recognize a wide range of defects with speed and consistency. When you pair this technology with a robotic arm that can act on what the cameras see, the benefits are considerable:

  • Higher accuracy. Computer vision systems catch more defects than humans. Vision-enabled solutions from Rapid Robotics, for example, regularly deliver a 99.98% quality approved rate.

  • Improved compliance. Automated visual inspection can capture important data for audits in heavily regulated industries as well as help identify why parts are rejected.

  • Higher productivity. Computer visions systems can inspect more parts per hour than humans, and they work around the clock, driving up efficiency. With one of our vision-enabled solutions, a manufacturer cut cycle time from 12 minutes to less than a minute per unit.

  • Operational flexibility. When computer vision systems take on bigger inspection workloads, it enables manufacturers to reassign human operators to more valuable, complex tasks.

Look into computer vision now

Vision-enabled robotics solutions can significantly improve part quality. The Rapid Machine Operator (RMO) from Rapid Robotics is fully integrated with vision systems from Elementary, Apera, Keyence, and other leading vision system manufacturers. RMOs can be ready within a matter of weeks, arrive pretrained and can be up and running in hours. To learn how a computer vision-enabled RMO can improve quality control, contact Rapid Robotics now for a free 30-minute automation consultation.


Rapid Robotics Content Team

We're a group of diverse humans who tell anyone who'll listen how Rapid Robotics can make it easy, fast and affordable for manufacturers to start automating. And while we believe in the power of automation and AI, we're real peeps who write every word ourselves!

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