Computer Vision mimics human eyesight and the brain’s ability to see, observe and understand large quantities of data in real time.
What is Computer Vision and its Real-World Applications?
Computer Vision is a field of Artificial Intelligence (AI) that programs machines to analyze images captured by connected equipment such as cameras and satellites. It is an image-recognition tool that processes images in order to derive meaningful information and make appropriate recommendations.
Essentially, Computer Vision is the AI equivalent of human eyes.
Combined with other AI functionalities, the machine mimics the brain’s ability to see, observe and understand large quantities of information with great accuracy.
For now, human eyesight is more developed and sophisticated than Computer Vision. Since they evolved over thousands of years, humans naturally learned how to analyze and understand the information contained in images. They can instinctively contextualize situations, tell objects apart, how far away they are, if they are moving, and if there is something wrong.
While humans perform these functions with retinas, optic nerves and a visual cortex, Computer Vision programs machines to see and analyze images with the help of cameras, data and algorithms. Thus, machines need to be programmed to learn how to see, understand and interpret information like humans.
Why bother teaching computers how to analyze images?
The machine’s competitive advantage lies in its relentless computing power.
Indeed, modern AI machines are capable of analyzing very large quantities of data very quickly. A typical AI machine can process thousands of images a minute. In comparison, a human can process a few dozen at most.
Furthermore, Computer Vision machines are programmed to methodically inspect every image in great detail to detect patterns, defects or issues. Humans can also do this, but with decreasing returns: after 7-8 hours of continuous analysis, your cognitive abilities wane. After five days of work, you need to rest for two days.
In contrast, a machine does not develop brain fatigue after hours of repetitive analysis, is never sick and will never develop the sudden urge to change occupations..
Thus, it is undeniable that well-programmed machines have the potential to infinitely surpass human capabilities.
Now that we understand what Computer Vision is, it’s important to explain how it actually works.
How does Computer Vision actually work?
Computer Vision programming is made possible thanks to a combination of two important AI tools: Deep Learning and Convolutional Neural Networks (CNN).
Deep Learning is an application of AI that reproduces the architecture of the human brain through an intricate network of artificial neurons. These neurons absorb vast quantities of data and analyze them thanks to sophisticated algorithms. The novel aspect of Deep Learning is that the machine’s algorithms enable it to learn by itself rather than requiring specific programming.
However, this process requires lots of data input to develop the machine’s self-learning abilities.
Basically, a machine will analyze data again and again until it’s able to distinguish and recognize different elements within images. For example, an engineer training a computer to recognize automobiles will feed it enormous quantities of automobile-related images.
Over time, the machine will learn how to recognize an automobile, how to distinguish between different types and brands of automobiles and how to spot an automobile with defects.
A Convolutional Neural Network (CNN) assists the Deep Learning software by breaking down images into pixels.
These pixels are then attributed tags and labels that are used to perform convolutions and predict what the machine is seeing.
A CNN is used to understand single images and works in two steps: first, it discerns hard edges and simple shapes; second, it runs iterations of its predictions and fills in information. The CNN runs a series of convolutions then checks the accuracy of its predictions until they become true. At that point, the machine is effectively “seeing” and recognizing images just like humans.
For analysis of video images, a Recurrent Neural Network (RNN) is used to help machines understand how a series of images relate to one another.
In addition to being able to identify elements of an image, Computer Vision has another important feature: text extraction.
What is text extraction?
Text extraction is the final feature of Computer Vision that needs to be mentioned.
In practice, it is the act of extracting text from an image in order to collect information and conserve a written trace.
If the machine is analyzing the image of an automobile, its algorithms will first identify the automobile and surrounding objects. Then, it will draw upon its past experiences to produce a report of what is it “seeing”.
For example, it may redact the following notes:
- Black automobile of Toyota brand
- Damaged front tires
- Broken windshield
- Presence of animal on windshield
- Conclusion: possible crash due to animal crossing
While the information presented by the machine appears straightforward, this feature is very useful for analyzing thousands of images quickly. The human brain, although capable of refined analysis, is unable to compete with this computational power.
Further, Computer Vision analyzes every pixel of an image. This means that no detail is ever ignored, which is especially useful when dealing with images containing large quantities of information to analyze.
For all these reasons, Computer Vision is revolutionizing virtually every sector of business and society.
What are the real world applications of Computer Vision?
Computer Vision is a rapidly growing market.
MarketsandMarkets’ research currently values the market at $15.9 billion and estimates that it will reach $51.3 billion by 2026, at a CAGR of 26.3%.
In fact, the technology is becoming so widespread that almost every sector of business and society is using it in some way.
Here is how Computer Vision is used in 5 different industries.
1. Leading technology companies are investing massively in Deep Learning and Computer Vision:
- Amazon’s Rekognition is a deep-learning image recognition platform that helps customers gain insight and new revenue opportunities from their image library.
- Google’s Cloud Vision API provides clients with advanced object and face detection services for reasonable prices.
- Microsoft Azure’s Computer Vision services provide text extraction, image understanding, spatial analysis and flexible deployment solutions.
- Apple currently has 66 job openings in its Computer Vision division.
- IBM’s Computer Vision team has the ambitious mission of enabling its AI platform to interpret visual content as easily as it does text.
2. In the automobile sector, manufacturers are using connected Computer Vision to develop their self-driving software
Tesla, the leader in autonomous driving technology, is abandoning IoT (Internet of Things) in favor of Computer Vision. The rationale is that connected cameras can process information in real time and with greater accuracy than electronic sensors.
Other manufacturers, such as BMW, Audi, and Volvo are following suit. They use connected cameras, lidar, radar, and ultrasonic sensors to help their self-driving cars analyze their environment and detect objects and obstacles, lane markings, traffic signals and other signs.
3. In the security industry, Computer Vision is used for facial recognition and pattern detection.
Police departments use the technology to survey urban environments, analyze the behavior of large crowds, detect suspicious activity and identify potential threats before they materialize.
Airports and ports use Computer Vision technology at security checkpoints.
At the height of the Coronavirus crisis, Chinese company Dahua Technology applied Computer Vision to cameras in order to detect people with fevers.
4. Did you know that 90% of all medical data is image based?
Thus, Computer Vision is proving to be extremely valuable for developing new diagnosis techniques, identifying health risks before they appear and assisting with surgery.
In a word, Computer Vision and Deep Learning are revolutionizing traditional healthcare.
5. Computer Vision is being applied to agriculture with great success.
An incredible example of successful Computer Vision application to agriculture is John Deere’s semi-autonomous combine harvester.
In 2019, the company presented the incredible $500K machine which uses AI and Computer Vision to analyze grain quality as its being harvested. If a particular grain samples is deemed of poor quality, the harvester automatically adjusts the threshing process to avoid harvesting it. In addition, the 20-ton harvester can harvest 15 acres an hour and uses AI to self-steer and automatically find the optimal route through the rows of crops.
There are just a few examples of how Computer Vision is used in the real world. There are dozens of other interesting use cases we could mention.
While Computer Vision is still in its infancy, it is poised to revolutionize the way we analyze and process data.