Rapid defect detection with computer vision

Learn how a one-hour prototype revealed what’s possible in automated quality assurance

In manufacturing, every minute spent on a bad part adds cost, especially when the defect could have been caught earlier. Defects are expensive: whether you’re paying someone to sort finished goods by hand or spending time troubleshooting processes upstream.

That’s where computer vision (teaching machines to see and understand images and videos) comes in. With the right setup, this type of AI-powered tool can catch issues early, reduce manual QA, and help you move faster with less waste.

But AI doesn’t have to mean months of planning or massive infrastructure. In this quick proof of concept (POC), our expert developers set out to show just how fast you can begin learning whether a computer vision solution might work for you.

A simple test: Detecting bolt defects in under an hour

We designed a small-scale experiment to simulate a common manufacturing challenge: spotting defects in threaded bolts.

The goal? Show that within a couple of hours, you can begin testing feasibility and receive early signals about what’s possible before committing major time or budget. It’s about moving forward with clarity, not about building a finished solution.

Here’s how we did it:

1. Captured footage: We used a standard iPhone to record bolts moving past a fixed position.
2. Collected and extracted data: That single video was split into about 300 still images.
3. Annotated examples: Roughly 100 images were labeled to highlight both clean bolts and visible defects like bad threads.
4. Trained the model: With those labeled examples, we trained a simple computer vision model to distinguish between good and bad bolts.
5. Tested the model: Finally, we ran a new video through the model to watch how it flagged defects in real time.

What this means for operations like yours

This wasn’t about building a finished product—it was about validating whether the concept works. And it does. Within a couple hours, we had a functioning test that showed real potential.

For QA and ops leaders, this kind of early validation can:

• Catch problems earlier in production
• Reduce time spent on manual inspections
• Lower costs from wasted parts and late-stage fixes
• Provide data that helps diagnose root causes upstream
• Give your team a head start on scaling automation

Computer vision isn’t just for tech giants. It’s increasingly accessible, and your competitors are likely already exploring it.

Curious about whether AI-powered defect detection is viable for your business? We can help you explore it quickly, then build a plan to scale if it makes sense.