Embedded Computer Vision Is Going to Transform Everything

By Jeffrey Goldsmith

VP of Marketing

Chooch AI

July 15, 2021


Embedded Computer Vision Is Going to Transform Everything

Digital transformation is the process of integrating digital technologies into all areas of your business – and nothing is more transformative than AI technology, especially computer vision, that can evaluate visual information faster and more accurately than people.

In fact, the most advanced computer vision strategies are currently leveraging video streaming to edge devices –  inspecting, analyzing, and evaluating visual data including pictures, videos, satellite images, and lab samples – to make instant assessments with a level of accuracy and cost-effectiveness that dwarfs the capabilities of human eyes. And as we train more and more AI models, computer vision will be applicable to almost every visual task.

What Do We Mean by Computer Vision and Embedded AI?

Until the last decade, the idea that computers could interpret any kind of visual information – with the same or better degree of acuity as humans – was seen as an impenetrable frontier of computer science. However, modern computer vision technology now consists of AI models on edge devices that can analyze and understand the confusing jumbles of pixels that form images and video – and they can perform the widest variety of visual tasks better than their human counterparts.

In fact, AI-interpreted camera feeds are currently so advanced that they can count cells under slides or identify defective bottle caps in factories far better than people, and they’re doing it in milliseconds. Those are just two narrow examples. Cameras connected to embedded AI on servers can check to see if store shelves are stocked or that construction workers are wearing their protective gear. At this point, if a job requires human eyes to make a determination, a well-trained computer vision system can probably do it better. Computer vision is basically infinite.

Where is Computer Vision Better Than Human Vision? 

Rather than saying just about everywhere, let’s look at why computer vision performs better than its human counterpart in many areas. First, we need to explore the limitations of human vision and how it gets in the way of accuracy.

For example, scientists have long known that human vision doesn’t provide an accurate and objective reflection of the world around us. As Denise Grady wrote for Discover Magazine, “The eye and brain work in a partnership to interpret conflicting signals from the outside world. Ultimately, we see whatever our brains think we should.” 

In fact, our brains are constantly filling in blind spots to create a seamless experience of the world around us – even if they have to “make up” the information to do it. The brain changes shading, alters colors, and unconsciously decides what we’re looking at. In many cases, we’re only perceiving an illusion that’s fraught with errors and inaccuracies. 

As an example, what do you see in the image above? A duck? A rabbit? Both are correct, but it’s impossible to see both images simultaneously. Choosing to see the rabbit momentarily loses the duck and vice versa (give it a try). 

These variances in perception don’t pose much of a problem in daily life – but they’re absolutely detrimental when performing visual tasks that require a high degree of accuracy, such as: 

  • Inspecting machinery for repair issues
  • Monitoring employees for PPE compliance
  • Checking infrastructure assets for rust and decay
  • Counting cells under a microscope
  • Checking store shelves for low inventory

Besides simply dozing off or getting distracted by your phone, why do we make errors in tasks? 

According to Michigan State University researchers, the visual cortex makes complex decisions, just like the higher levels of the brain, and it usually happens unconsciously. In fact, the visual cortex decides what we’re going to see whether it’s an accurate reflection of reality or not. This can produce confirmation biases and errors when human workers interpret visual data – and these biases and errors get worse when we’re bored, fatigued, and distracted.

As Danil Myakin, co-founder of Squilla Capital, puts it: 

“People always remain biased and emotional, regardless of whether they are aware of it or not. Everyone knows that people make mistakes.” 

In contrast to the error-prone nature of human interpretations, computer vision sees and understands visual data more objectively – rendering the same results again and again with absolute consistency. Let’s take a look at some of the primary reasons why computer vision models are better at performing visual tasks than humans:

  • Consistent: The quality of visual AI task performance will not vary based on the time of day or how long the AI has been running. Nor will the AI become bored, tired, distracted, sick, hungover, or frustrated –  which are unpredictable variables that negatively impact human performance. None of these “human” factors will ever affect the consistency and accuracy of computer vision task performance. 
  • Always Available: Visual AI systems do not take lunch breaks, sick days, vacations, or quit their jobs. They are always available, 24-hours a day, 7-days a week. 
  • Scalable: As the volume of visual monitoring and evaluation tasks increase, organizations don’t need to hire, source, or train new employees. With a touch of a button, they can infinitely replicate and scale an existing computer vision model to complete a higher volume of work. 
  • More Accurate: Computer vision systems can track more variables simultaneously. Instead of focusing on three security camera feeds at a time, they can look at hundreds or thousands, and never miss a security-related incident. Rather than identifying one face, they can identify hundreds or thousands of faces in a crowd of people. Likewise, instead of taking 30 minutes to count one cell at a time under a microscope, computer vision instantly counts all of the cells at once. 

Ultimately, computer vision offers the capacity to simultaneously and objectively track an infinite number of visual factors with greater attention to detail than a human could ever hope to achieve. It doesn’t produce inconsistent analyses as a result of getting tired, distracted, or bored – and it’s infinitely scalable.

When you consider the many advantages of using visual AI instead of human eyes, doesn’t it follow that computer vision will soon become a competitive necessity for businesses to streamline workflows, boost profit, and free up human workers for more important tasks?

Radical Digital Transformation Has Changed the World Before

If you still can’t see how edge AI with cameras is going to change everything, think of the radical changes we’ve seen as a direct result of global smartphone adoption – which has profoundly altered the way we communicate with each other and entertain ourselves. 

Aside from the fact that they are simply a better and easier way to communicate and entertain ourselves, a primary reason for the success of smartphones relates to them becoming dramatically more affordable and accessible. Adding texting and maps and dating and payments to smartphones helped to cement their absolute necessity.

Similarly, computer vision is becoming more affordable and accessible, paving the way for AI-enabled cameras to become a competitive necessity for improving speed, efficiency, and accuracy for countless visual tasks in business, science, military, government, and more.

Now, organizations can set up unique visual AI systems in a matter of days – easily and inexpensively – whether the use case involves slip-and-fall detection, smoke and fire alerts, or watching the back door to make sure inventory doesn’t get stolen. When AI detects a fall, a fire or a theft in progress, the data generated triggers an alert, a digitally transformative event.

Rapid Development and Deployment of Visual AI Models

Just like human children develop their minds to see, analyze, and interpret their surroundings, a visual AI system requires training. It once took years to train a computer vision model to perform basic visual tasks. While most visual AI strategies still require 6 to 9 months to train and deploy, we are now seeing newer, faster, and easier-to-use visual AI platforms coming available.

Now, computer vision platform allows companies to develop and deploy AI models in just 6 to 9 days. Chooch AI achieves this high-speed level of deployment by offering a library of prebuilt visual AI models for fire, falls, faces, defects, cell counting, product inventory, and other use cases. Choose an existing model for instant deployment like Human Fall Detection, add additional layers of training for more nuanced applications – or train a completely new model when required.

Chooch AI’s automation tools for generating and annotating images speed up training as well, offering organizations tremendous agility and affordability to rapidly develop novel computer vision solutions. 

Ultimately, as computer vision solutions become easier, faster, and more affordable to train and deploy, embedded vision technology will spread like wildfire – changing society even faster than smartphones, since no special equipment beyond a camera and device is required.