As the AI or Artificial Intelligence scenario changes, in most cases, too fast, industrial vision systems must follow suit. These involve the automated quality inspection systems of today and the autonomous robots of the future.

Whether you are an OEM or Original Equipment Manufacturer, a systems integrator, or a factory operator, trying to get the maximum performance out of a machine vision system requires future-proofing your platform. This is necessary so that you can overcome the anxiety of having launched a design only months or weeks before the introduction of the next game-changing architecture or AI algorithm.

Traditionally, the industrial machine vision system is made up of an optical sensor like a camera, lighting for illuminating the area to be captured, a controller or a host PC, and a frame grabber. In this chain, the frame grabber is of particular interest. This device captures still images at a higher resolution than the camera can. High-resolution images simplify the analysis, whether by computer vision algorithms or by AI or artificial intelligence.

The optical sensor or camera connects directly to the frame grabber over specific interfaces. The frame grabber is typically a slot card plugged into the vision platform or PC. It communicates with the host over a PCI Express bus.

Apart from its ability to capture high-resolution images, the frame grabber also has the ability to trigger and synchronize multiple cameras simultaneously. It can also perform local image processing, including color corrections, as soon as it has captured a still shot. While eliminating latency, it also eliminates the cost of transmitting images to the cloud for preprocessing, while freeing the host processor for running inferencing algorithms, executing corresponding control functions, and other tasks like turning off lights and conveyor belts.

Although the above architecture makes the arrangement more complex than some newer types that integrate various subsystems in the chain, it is much more scalable. It also provides a higher degree of flexibility, as the amount of image-processing performance achieved is limited only by the number of slots available in the host PC.

However, machine vision systems relying on high-resolution image sensors and multiple cameras can face a problem with system bandwidth. For instance, a 4MP camera needs a throughput of about 24 Mbps. PCIe 3.0 interconnects offer roughly 1 Gbps per lane data rate.

On the other hand, Gen4 PCIe interfaces double this bandwidth to almost 2 Gbps per lane. Therefore, you can connect twice as many video channels on your platform without making any other sacrifices.

However, multiple camera systems ingesting multiple streams can consume bandwidth rather quickly. Suppose you are adding one or more FPGA acceleration or GPU cards for higher accuracy, and low latency AI or executing computer vision algorithms. In that case, you have a potential bandwidth bottleneck on your hands.

Therefore, many industrial machine vision integrators make tradeoffs. They may add more host CPU to accommodate the shortage of bandwidth, use a backplane-based system to make the accelerating cards play a bigger role, or change over to a host PC with integrated accelerators. Regardless, the arrangement adds significant cost and increases power consumption and thermal dissipation. Modularizing your system architecture can safeguard against this.