A robust vision system

“Vision systems don’t work.”

How many times have you been told that? More than a couple, I’ll wager. And sadly, it’s too often true. You set the system up, check it’s making the correct decisions and walk away. A few weeks later, (maybe sooner,) you’re back because something changed.

This is the big problem: machine vision is totally repeatable, even if something changes. That’s a problem, because human inspectors will adapt almost instantly to acceptable variations, but machine vision? It’s so dumb it will just kick out all your good product, or worse, pass your bad product.

What’s to be done then? I could write a book about this – and perhaps I will – but for now have a look at “Ensuring Reliability in Vision Systems,” published in the November 2011 Assembly magazine.

This runs through the main issues in building a robust vision system: assess process variation, write a detailed specification, resist pressure to add additional inspections, get the lighting right, throw plenty of pixels at the task and so on. These are all good points and should be taken on-board.

But, (you knew there was a “but” coming, didn’t you?) I can’t resist nit-picking, so let me mention a couple of points.

First, there’s a big old typo: “Dumb cameras require an external image sensor, which initiates image capture.” This makes little sense to me: the sensor is always inside the camera, whether smart or dumb. Are they talking about triggering?

Second, Rick Roszkowski of Cognex says, “Always bring enough pixels to the party,” before going on to suggest a 10:1 rule – 10 pixels to span the tolerance band. Those aren’t bad rules of thumb, but:

1. Be aware that more pixels equals lower frame rates and more processing time – it’s a tradeoff.
2. I think that 10:1 rule is derived from the old metrology rule-of-thumb. I don’t disagree but I would dearly like to see some math applied to the issue of how many pixels are needed to produce a sufficiently repeatable measurement.

So, with those two little quibbles out the way, let me say again, “Ensuring Reliability in Vision Systems,” is a good read. Take a look.