Computer vision is a kind of AI technology aimed to collect data from images or videos and process this data for analysis and decision-making. Computer vision algorithms enable computers to see the picture/video, observe what is happening, and make data-based decisions on further actions of the system or an operator.
Thinking technically, computer vision algorithms should be totally autonomous and work even better than human eyesight. To reach this goal, huge systems are built to test-drive what engineers and scientists could create.
How it works
Just like with any AI testing, the “seeing” computer must easily solve some tasks: Automatically identify objects it sees in the image and recognize their location. Split the objects by category and relate them to each other Understand what is going on in the video or picture and describe it.
As for human eyesight, we know that our retina accepts the image and sends it to the brain. The engineers based the computer vision algorithms on this principle.
Cameras, data, and algorithms put together enable machines to analyze thousands of pixels with the designated color values, convert them into code and count the numbers until getting the result.
You can train a model to recognize the object, such as a person or animal, the number of objects, how far the object is, and how it acts.
After the machine understands, what is going on, it responds by ordering the system to take a pre-configured action or notifying an operator that the required object or action has appeared. Computer vision algorithms are especially useful in case the picture or video is too large to be processed by a human. See some examples below.
The use of computer vision
Probably, you’ve heard that OCR and handwriting recognition are based on computer vision algorithms. This is a classical application of a tool. Outside of this, there are many fields where computer vision is used daily. From energy and utilities, up to entertainment — computer vision is everywhere.
Healthcare and diagnostics.
Such dangerous diseases like lung cancer are essential to be found early. Unfortunately, doctors can only be 65% sure of the diagnosis accuracy. Meanwhile, the computer vision algorithm offers up to 95% of accuracy. Such diagnoses like tumors, neurological malfunctioning, etc. can be made faster, safer, and more efficiently thanks to computer vision.
Detecting defects at the factory can be crucial for huge sets of products. Mistakes result in losing millions, and reputational damages. Working on a micro-scale, computer vision algorithms use real-time data to find items of poor quality, analyze them to predict machine downtimes, and control employee work.
Retail and marketing
Modern cameras not only prevent shoplifting. With computer vision algorithms working 24/7, retailers can determine their target audience, get insights into aisle arrangements, and control inventory.
An advertisement becomes agile and adaptable: cameras on billboards can recognize eye movements and facial expressions, understand gender and age, and provide an advertisement based on these real-time characteristics.
Computer vision provides gestures and facial recognition. Talking about schools and kindergartens, such smart cameras have saved thousands of kids from sex offenders and other threats already. Street surveillance helps recognize and arrest criminals trying to escape the law.
Automotive and airlines.
Some airlines use face recognition not for safety only: they identify and check in passengers with cameras, saving time and lowering ticket costs. Meanwhile, self-driving cars and premium cars with assistance systems use computer vision to detect objects in their path and react quickly.
In many industries — from agriculture to public safety, computer vision plays a key role to build the processes and reach the goal faster.
Starting integration of computer vision at your company
Now, as it becomes clear that computer vision becomes an important part of our lives, you probably wonder how the process of integration is built. Here are the steps to start:
Collect the data and analyze it. Identify use cases applicable to your computer vision goal, and find potential issues to cover Build the plan and make a pilot. Estimate your costs and benefits. Prioritize use cases and create a minimum viable pilot to ensure it can cover your needs the way you expected. Start collecting resources and capabilities. Identify data you need, count the time and money resources and consider two options to start with: hiring your own team or ordering the project from an outsourced expert. To succeed with your idea, engage truly experienced professionals.
Why outsourcing computer vision can be helpful
Hiring an AI expert can cost a fortune. That is why many companies choose to outsource the vendor for end-to-end integration. Though, the money saving is not the only reason to hire an expert team to complete your computer vision task. Outsourcing the company offering computer vision solutions can lead to:
Working with the company caring about their reputation provides you with a guarantee that in case any issue arises, it will be resolved soon.
Higher process efficiency
Instead of focusing on computer vision algorithms only, you can use your human resources to resolve vital tasks of your business and ensure the resulted computer vision tool will have enough resources to be implemented efficiently.
Data security and world-class Infrastructure
Ensuring your vendor works in accordance with data privacy standards and engages world-class professionals, you can get an even better result than expected, faster and more efficiently.
As AI and computer vision integration becomes more and more popular, finding a trusted partner becomes easier. Choose the most experienced vendors like Brivian or IBM, prepare your resources well, and start making your processes better.