The Machine Vision camera “Sleepability factor”!?

The sleepability factor, or how saving $50 on a machine vision camera could cost you thousands!

As an independent machine vision camera distributor, we are asked about the manufacturers we represent. Out of all them, we have chosen to only carry products from a few machine vision camera and lens companies of which we have not really changed this over the 20 years we have been in business. Why is this?

These days, there are probably over 30 different camera manufacturers making products for the machine vision marketplace, many using the identical image sensors. Considering anyone can just put up a web page and start selling, how can a user know which product to purchase? Are there really any differences? Should I just purchase on price? Should I buy from a distributor or from the manufacturer direct? All great questions, that we will attempt to answer.

The very simple and quick answer is that if you just need to get a image in good light, pretty much any camera from any vendor will do that job. No matter if it is from a large company or a 2 person startup, when you take the product out of the box, you should see a “good” image.

But if you said, “I want this camera to run 24/7 for the next 5 years, I want to be able to develop complex software to integrate into my machine, I need the image sensor plane to be within a certain tolerance for each machine,” this changes the situation.

IDS imaging camera — IDS Imaging USB3 cameras

The reason we have chosen the camera manufacturers we sell products from is because each of them has a proven track record of reliability. Each of the companies we represent ships 6 figures of cameras per year. IDS Imaging for instance ships close to 200,000 cameras per year and has a return rate of under 0.3%.

Allied vision camera — Allied Vision GigE Cameras

Allied Vision was the first company to incorporate the Precision Timing Protocol (PTP) which allows for precise multi camera sync, enabling our clients to not only make sure the application will work, but it doesn’t take years to develop it.

Dalsa line scan cameras — Teledyne Dalsa Line Scan cameras

Teledyne Dalsa, besides being a leader in line scan technology, has a SDK that has been built upon for over 30 years.

JAI’s prism technology is so good that its competitors actually have JAI

manufacture for them. This isn’t to say there aren’t other camera companies with such characteristics. There are, and many of the other camera companies have excellent products as well. It is just that we have chosen these companies, and we have stood with them for 15+ years for good reasons… sleepabilty!

What does this mean to you as a client? Yes, you can purchase a camera from any vendor, which on a $500 camera, you might even be saving $50 a camera. If you purchase 100 cameras a year, this adds up to a reasonable savings of $5,000. But what happens if your machine, which you sell globally, has a camera that fails. What is the cost of the line going down at your client? How do you look in your client’s eyes? What is the cost for you to fix it?

If you had your choice of buying a camera with the same characteristics at roughly the same price, but one company makes 20,000 a year, and the other makes 200,000 a year, which would you choose? The same is true if you can choose between a company that has offices all over the world, or just in one country. Or one that has many application engineers to answer your questions, or just one.

We just want to point out that if you are making a purchase on price alone, depending upon your circumstances, it might not really be a savings at all. In fact, it might actually not only be costing you money, but it might even be costing you your sleep!

1st Vision’s sales engineers have over 100 years of combined experience to assist in your camera selection. With a large portfolio of lenses, cables, NIC card and industrial computers, we can provide a full vision solution!

March 22, 2018March 26, 2018

Which Industrial camera would you use in low light?

Our job as imaging specialists is to help our customers make the best decisions on which industrial camera and image sensor works best for their application. This is not a trivial task as there are many data points to consider, and in the end, a good image comparison test helps provide the true answer. In this blog post, we conduct another image sensor comparison for low light applications testing a long time favorite e2V EV76C661 Near Infra Red (NIR) sensor to the new Sony Starvis IMX178 and Sony Pregius IMX174 image sensor using IDS Imaging cameras.

An Industrial camera can be easily selected based on resolution and frame rates, but image sensor performance is more challenging. We can collect data points from the camera EMVA1288 test results and spectral response charts, but one can not conclude on what is best for the application based on one data point. In many cases, several data points need to be reviewed to start making an educated decision.

We started this review comparing 3 image sensors to determine which ones would perform best in low light applications.

Below is a chart comparing the e2v EV76C661 NIR, Sony Starvis IMX178 and , Sony Pregius IMX174 image sensors found in the IDS Imaging UI-3240NIR, UI-3880CP and UI-3060CP cameras using EMVA1288 data to start. This provides us with accurate image sensor data to evaluate.

image sensor comparison — Table 1: Sensor comparison data

Spectral response cufves — Camera Spectral Response curves

We also look at the Quantum Efficiency (QE) curves for the sensors to see the sensor performs over the light spectrum as seen to the left. (As a note, QE is the conversion of photon to an electrical charge (electrons)

For this comparison, our objective is to determine which sensor will perform best in low light applications with broadband light. From table 1, the IMX178 has very low absolute sensitivity (abs sensitivity) with taking ~ 1 photon to help make a adequate charge, however the pixels are small (2.4um), so maybe not gather light as well as larger pixels. It does have the best dark noise characteristics however. In comparison, the e2V sensor has 9.9 photons for abs sensitivity (not as good as 1 photon) and has a larger pixel size (bigger is better to collect light). The IMX174 proves to be interesting as well with the largest pixel of 5.86um and the highest QE @ 533nm.

Using the data from the spectral response curves however, helps give us more insight across the light spectrum. Given we are using a NIR enhanced camera, we will have significant more conversion of light to a create a charge on the sensor across most of the light spectrum. In turn, we expect we’d see brighter images from the e2V NIR IDS UI-3240 NIR camera.

As a note, one more data point is to look at the pixel well depth. Smaller pixels will saturate faster making the image brighter, so if other variables were close, this may also be taken into consideration.

As one can see, this is not trivial, but evaluating many of the data points, can give us some clues, but testing is really what it takes! So, lets now compare the images to see how they look.

The following images were taken with the same exposure, lens + f-stop in the identical low light environment. In the 2nd image, the e2v image sensor in the IDS-UI-3240CP NIR provides the brighter image as some of the data points started to indicate. The IDS UI-3060CP-M (IMX174) is second best.

In low light situations, we can always add camera gain, but we pay the price of adding noise to the image. Depending on the camera image sensor, some have the ability to provide more gain than others. This is another factor to review when considering adding gain. We need to also take into account read noise as this will get amplified with gain. Our next part of our test is to turn up the gain to see how we compare.

The following set of images was taken again with the same lens + f-stop, lighting, but with gain at max for each camera.

The IDS-UI-3060CP-M has the highest gain available, but still keeps the read noise relatively low with 6 electrons. This in low light WITH gain, gives us a nice image in nearly dark environments.

Conclusion
We can review the data points until we are blue in the face and they can be very confusing. We can however take in all the data and help make some more educated decisions on which cameras to test. For example in the first test, we had a good idea the NIR sensor would perform well looking a the QE curves along with other data. In our second test, we may have seen the UI-3060CP had 24X gain vs others still with low read noise, giving an indication, we’d have relatively clean image.

In the end, 1st Vision’s sales engineers will help provide the needed information and help conduct testing for you! We spend a lot of time in our lab in order to provide first hand information to our customers!

1st Vision is the leading provider of industrial imaging components with over 100 years of combined imaging experience. Do not hesitate to contact us to discuss your applications!

Just a few foot notes regarding this blog post:

Magnification of the images differs due to sensor size. Working distance of the cameras was kept identical in all setups and focused accordingly with distance.

This topic can be very complex! If we were to dig in even deeper, we’d take into consideration charge convergence of the pixel which effects sensitivity aside from looking at just QE!.. That’s probably another blog post!

As a reference, this image was taken with an Iphone and set to best represent what my eye viewed during our lab test. Note that the left container with markers was non-distinguishable to the human eye

Clipart courtesy of clipartextra.com

March 22, 2018July 2, 2018

Allied Vision Manta G-040 & G-158 provide great replacements to legacy CCD cameras

Allied Vision is expanding its line of Manta machine vision cameras, releasing the Manta G-158 and Manta G-040 GigE Vision cameras. Featuring the second generation Pregius CMOS image sensors from Sony. These are great replacements for Legacy Sony ICX424 and Sony ICX445 image sensors found in the Manta G-032 and Manta G-125 cameras.

The G-158 camera features the 1.58 megapixel Sony IMX273 image sensor, which has a 3.45 µm pixel size and achieves a frame rate of 75.3 fps. The Manta G-040 camera features the 0.4 megapixel Sony IMX287 image sensor, which has a 6.9 µm pixel size and achieves a frame rate of 286 fps. Higher frame rates can be achieved on both models in burst mode.

Allied Vision Manta Specifications and comparisons to older Sony CCD sensors as follows:

Allied Vision Manta features include:

Power over Ethernet options (PoE) with Trigger over Ethernet for single cable solutions
Angled Head and Board level variations allowing for custom OEM designs
Video-Iris lens control for challenging lighting conditions
Three look up tables (LUT)
Gige Vision compliant with support for popular third party image processing library’s including Cognex VisionPro, Mathworks, MATLAB and National Instruments

To Learn More about the Allied Vision Manta cameras

View more information on the G-158.
View more information on the G-040.

UPDATE: See this new video from Allied Vision (6/19/18)

Additional References:

What are the attributes to consider when selecting a camera and its performance?

Need higher resolution? – Learn about Allied Vision’s high-resolution Prosilica GT now with 12, 16 and 25MP ON Semi PYTHON sensors

Visit our “Knowledge Base” to learn more about camera technology, machine vision lenses and lighting.

February 21, 2018June 4, 2023

How much resolution do I lose using a color industrial camera in a mono mode? Is it really 4X?

Many clients call us about doing measurements on grey scale data, but want to use a color machine vision industrial camera because they want the operator or client to see a more ‘realistic’ picture. For instance, if you are looking at PCBs, need to read characters with good precision, but also see the colors on a ribbon cable, you are forced to use a color camera.

In these applications, you could take out a monochrome image from a color sensor for processing, and use the color for cataloging and visualization. But the question is, how much data is lost by using a color camera in mono mode?

First, the user must understand how a color camera works, and how it gets its picture. Non 3-CCD cameras use a Bayer filter, which is a matrix of red, green, and blue filters over each pixel. For each group of 4 pixels, there are 2 greens, 1 red and 1 blue pixel. (The eye being most sensitive in Green, has more to simulate the response).

To get a color image out, each pixel out is a computation of a weighted sum of its nearest neighbor pixels which is known as Bayer interpolation. The accuracy of the color on these cameras is a result of what the original image was, and how the camera algorithms interpolated the set of red, green and blue values for each pixel.

To get monochrome out, one technique is to have the image broken down into Hue, Saturation, and Intensity, with the intensity taken as the grey scale value. Again, this is mathematical computation. The quality of the output is dependent upon the original image and the algorithms used to compute the output.

An image such as the above will give an algorithm a hard time as you are flipping between grey scale values of 0 and 255 for each pixel (assuming the check board lines up with each pixel). Since the output of each pixel is based on it’s nearest neighbors, you could be replacing a black pixel with 4 white ones!

On the other hand, if we had an image with a ramp of pixel values, in other words, each pixel was say 1 value less than the one next to it, the average of the the nearest neighbors would very close to the pixel it was replacing.

What does all this mean in real world applications? Let’s take a look at a 2 images, both from the same brand of camera where one is the using the 5MP Sony Pregius IMX250 monochrome sensor, the other is using the same sensor, but the color version. The images were taken with the same exposure and identical setup. So how do they compare when we blow them up to the pixel level after we take the monochrome output from the color camera and compare it to the monochrome camera?

Grey Scale Analysis — (Left) – Color Image ——————————- (Right) – Monochrome Image

In comparing the color image (Left), if you expand the picture, you can see that the middle of the E is wider. The transition is not as close to a step function as you would want it to be. The vertical cross section is about 11 pixels with more black than white. Comparing the monochrome image (Right), the vertical cross section is closer to about 8 pixels.

Conclusion:

If you need pixel level measurement, and there is no need for a color image, USE A MONOCHROME MACHINE VISION CAMERA.

If you need to do OCR (as in this example) the above images using color or monochrome would work just fine. This is given you have enough pixels to start and your spatial resolution is adequate.

CLICK HERE FOR A COMPLETE LIST OF MACHINE VISION CAMERAS

Do you lose 4x in resolution as some people claim? Not with the image I have used above. Maybe with the checkerboard pattern, but if you can have multiple pixels across your image to measure, you might be ok in with using a color camera and is really application dependent! This post is to make you aware of the resolution loss specifically and 1st Vision can help in making decisions by contacting us for a discussion.

1stVision is the leading provider of machine vision components and has a staff of experienced sales engineers to help discuss your application. Please do not hesitate to contact us to help you in calculating the resolution you need to calculating focal lengths for your application.

Related links and blog posts

How does 3CCD cameras improve color accuracy and spatial resolution over Bayer cameras

Calculating resolution for machine vision

Use the 1st Vision camera filters to help ID the desired camera