Sensor sizes explained

Matching a lens to a camera is a headache. The problem is in finding a lens that will put the whole area viewed – the field of view – onto the camera sensor. If sensors were all the same size life would be simple, but unfortunately they aren’t. If you look at the specs you’ll see them referred to as 1/3”, ½” and so on, but do you know what this means?

Brian Robertson of PPT Vision recently posted one of the best explanations of this that I’ve ever read on the PPT blog. “Machine Vision Cameras and Imager Chip Sizes” (April 18^th, 2012,) provides a concise explanation of why sensors are described in this seemingly archaic manner. It also helps clarify what a 1 1/8 sensor actually is.

Once you understand what the numbers mean selecting a lens becomes a little easier.

Vision sensor application

Vision sensors, such as the Checker from Cognex, don’t seem to get the respect they deserve. I think they’re terrific tools, if used in the right application. The problem lies in knowing when they are the best tool to use.

To help, here’s an interesting application story I stumbled across on the Cognex web site: “Vision Sensors Error-Proof Oil Cap Assembly.”

In this case, the selling points for the Checker were use of ladder logic for easy integration and low cost. There’s no mention of the high frame rate capability, which I consider one of the most interesting features of the sensor.

Why do I like the high frame rate? Go back and read “In praise of high frame rates” (6/28/10) for a reminder.

Pixel pitch and pixel spacing

In “MTF and high resolution sensors” I discussed the impact of pixel pitch on resolution. Having received a question on this, I’d like to explain briefly why its pixel pitch that matters and not pixel size (although they are of course related,) and tell you where to find the information.

The size of a pixel gives you an indication of its light-receiving area. However, in discussing resolution it’s the spacing between the pixels that matters. The equation is:

Fmax-practical = 1/(4 x pixel pitch)

Where Fmax-practical is the maximum line-pair frequency that the sensor can resolve.

I gave the example of the 5Mp Sony ICX625 CCD which has pixels of 3.45 microns. However, the pixel pitch is around 4 microns. Why is the pitch greater than the pixel size? Well not all of the silicon collects light. In effect, there’s a border around each pixel, or to put it another way, there’s a space between neighboring pixels.

To determine the pitch you need the dimensions of the active region of the CCD, and for that you need to sensor’s spec sheet. Strangely, I couldn’t find it on Sony’s web site – they seem to want to keep it secret – but it's out there if you look hard enough. Once you've tracked it down you'll learn that the ICX625 measures 9.93 mm in the horizontal direction, and there are 2448 pixels in that length, which is how we get the pixel pitch of 4 microns. As a sanity check, you could do the following: given that the sensor is a 2/3” format with a 4:3 aspect ratio and a diagonal of 11.016mm, work out the length of the sides and calculate the pitch from there.

Hope that makes things clear. And by the way, I do welcome comments, so if you’ve got questions, complaints or anything you want to share, please make use of the “Comment” function.

Simple is good

Does your application really need a machine vision system? Yes, as Engineers we love to play with high tech toys, but chances are, you’re short of time and need to implement a solution as fast as you can. So why not start by looking at basic photoelectric sensors rather than smart cameras and vision systems?

Many companies compete in this market – Google will find them, I’m sure – but one that caught my eye recently is EMX Industries. In “Color, Contrast, and Luminescence Sensors: Which One Should You Choose?” by Bill Litterle of EMX, published on the Sensors web site November 1, 2008, there’s an interesting discussion of the various types of sensors available and their capabilities. For instance, a luminescence sensor can be used to identify the presence of grease on a part. Have you tried doing that with vision?

My point is that our job is to solve problems. Given that there’s no shortage of those, (not where I work, anyway,) we need to put a solution in place as quickly as possible, then move on. Sometimes a simple sensor is all that’s needed.