Embedded hardware to threaten expensive inspection systems
The low cost of embedded hardware is set to change the face of machine vision, Jeff Bier, founder of the Embedded Vision Alliance, has commented in an article for Imaging and Machine Vision Europe.
He warned that ‘companies selling rather expensive inspection systems for manufacturing have to be mindful of the fact that increasingly – not in all cases, but in some – the same job is going to be done by much less expensive hardware.’
Inexpensive computing platforms are now available, like the Raspberry Pi 3 that costs $35 and has plenty of processing power for computer vision, or Xilinx’s Zynq board that combines Arm and FPGA processors.
‘You can either run from it and try to protect your niche, which rarely works, or you can embrace it and say it’s going to be less about hardware and making money from expensive cameras, and more about the software and the enterprise IT aspect of the application,’ commented Bier in the article. ‘For the companies that do embrace this, it will open up all kinds of new opportunities to use vision where we never could afford to before.’
As the cost of hardware comes down, Bier feels the value will shift to the software. He said that a technique called deep learning, which is gaining traction in computer vision, could alter the way vision algorithms are developed.
Deep learning techniques use data to train the algorithm to discriminate between features in an image, rather than writing an algorithm for a specific task. Google and Twitter are using the technology in web-based image recognition.
Bier commented: ‘This [deep learning] is a promising, but also a somewhat threatening development. It’s promising because it means that rather than every problem needing to have a hand-crafted algorithm that could take years of effort, now there is the potential to use a much more generalised structure, as long as there is enough data. This is a big part of how the algorithm development bottleneck will be broken, and it will enable proliferation of vision into many new applications, including many new factory applications.
‘The threatening part,’ he continued, ‘is that if you are one of those experts who have built a career around developing algorithms, it’s pretty disconcerting to find there’s a more general way of solving the problem. Instead of algorithm expertise, we need people who can collect data and marshal that data through the training process, which of itself is a bit of a black art.’
The Embedded Vision Alliance’s website http://www.embedded-vision.com/ contains more information about deep learning techniques, including a keynote presentation given by Yann LeCun, director of AI research at Facebook, at the 2014 Embedded Vision Summit on convolutional networks.
‘There are nowhere near enough skilled engineers who understand computer vision to build all of these fantastic applications,’ Bier commented. ‘Deep learning is a huge help with that. It’s not a magic bullet, but it’s a huge help.’