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A year and a half ago I penned an article in this publication touting the merits of Gigabit Ethernet (GigE) cameras for machine vision purposes. The low cost and raw data bandwidth of GigE was not news, but the applicability of the technology to real, high-value, mission-critical machine vision applications was not at all clear back then.
After 18 months of experimentation and deployment, I’m thrilled to report that GigE cameras live up to all of my wildest expectations for use in machine vision. I don’t want to use anything else ever again.
The cameras are easy to set up, full-featured, lower cost-$450 for a machine vision grade video graphics array (VGA) camera-and easy to interface using supplied drivers and plug-ins available for all cameras and major machine vision applications.
But there are still some fears and concerns that persist in the vision community, so I would like to describe and debunk the more prevalent ones.
Network CongestionThe general networking rule of thumb is to limit total traffic to 80% of the link speed. As long as you stay there or well below there, system limitations will not be dictated by GigE; they will be dictated by camera speed, operating system (OS) overhead or central processing power (CPU).
For example, take a 640 by 480 8 bit mono camera running at 60 frames per second (fps). This data stream requires 640 by 80 by 8 by 60 = 147 megabits per second (Mbps), just 15% of a GigE link. You can see why handling four of these cameras per link is typically not much of an issue.
In higher performance a 2,000 three-sensor color line scan camera running at 5,000 lines per second requires 2,048 by 3 by 8b by 5,000 = 246 Mbps, or a quarter of a GigE link. We regularly run these in pairs through a switch on a single GigE port and have never experienced an imaging issue.
Lost PacketsIt is possible to configure GigE cameras in ways that will guarantee network congestion and, therefore, flaky behavior such as lost packets or an overburdened operating system. But it is much harder than one would think. In one round of testing, we set up a network with four 640 by 480 mono cameras running at 60 frames per second either using triggers or free running. This is a typical high-performance machine vision setup-240 images per second coming from four cameras continuously.
Whether we routed these cameras through a switch to a single GigE port or separately connected them directly to individual GigE ports, we were incapable of inducing any lost images or even lost image packets. Ethernet is a very reliable data transfer technology and has much more R&D investment and real-world runtime than any other camera interface technology ever used in history. Think about it.
Triggering and StrobesMachine vision needs external triggers and strobes. External triggers are signals that come from sensors or other electronic systems that tell the system when to take the picture. Strobe signals are generated at the appropriate time to fire a very short light pulse (strobe) to freeze motion much more consistently than a simple camera shutter can.
In the GigE model, the trigger signal is routed directly to the camera so that there is minimal latency from the trigger generation to the image acquisition. Similarly, the strobe signals are generated directly from the cameras to ensure that their timing is precisely correct for the image being acquired. After acquisition, the camera digitizes the image and transfers it at 1 gigabit per second (Gbps) to the analysis computer. Multiple camera asynchronous applications, always the bane of machine vision existence, are actually no harder than anything else with GigE.
Operating System WoesMy integration company works in real-time controls, embedded systems, Linux and custom operating systems. Many customers still specify Windows though, due to other considerations in the integration process.
We’ve developed high-speed area and line scan applications using GigE cameras in both Linux and Windows, and have had excellent results using both.
While the Linux system has a higher theoretical throughput, we can still run four-camera 60 fps applications on Core 2 Duo processor running Windows XP and accomplish basic image processing and metrology without losing images or packets. So while Windows may not be ideal, it is certainly a serviceable platform for many vision applications and offers special advantages in many cases.
PowerToday, most GigE cameras still require a separate power cable to supply 12 volts or 24 volts to power the camera. The Power over Ethernet (PoE) standard, however, is becoming ubiquitous and most camera vendors are planning their new camera lines to use PoE. With PoE, we can power the camera and communicate with it using 100-foot cables that cost in the tens of dollars. This is a huge step forward.
SupportAll of the major machine vision companies and software products now offer support and advice for using their products with GigE cameras. The GigE Vision standards from the Automated Imaging Association (AIA) are widely supported and allow mixing and matching of different vendors’ cameras and drivers within the same application framework. This is true mix-and-match, whether the vendors like it or not, and it is good for machine vision consumers.
So, the obstacles to GigE vision and the fears of the past are falling by the wayside. Don’t be afraid of GigE cameras, and don’t fall for the old-school conventional wisdom on GigE camera limitations.
Conventional wisdom is often just the sum total of all of the things that used to be true. I wonder what non-GigE cameras will even be available in another 18 months.