Size of Industry
$19,100,000,000
What is it?
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image.
Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.
Computer vision is used in industries ranging from energy and utilities to manufacturing and automotive – and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.
HOW does it work?
Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.
Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).
Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.
A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.
Much like a human making out an image at a distance, a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions. A CNN is used to understand single images. A recurrent neural network (RNN) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another.
Use Case
Today’s AI systems can go a step further and take actions based on an understanding of the image. There are many types of computer vision that are used in different ways:
Image segmentation partitions an image into multiple regions or pieces to be examined separately.
Object detection identifies a specific object in an image. Advanced object detection recognizes many objects in a single image: a football field, an offensive player, a defensive player, a ball and so on. These models use an X,Y coordinate to create a bounding box and identify everything inside the box.
Facial recognition is an advanced type of object detection that not only recognizes a human face in an image, but identifies a specific individual.
Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.
Pattern detection is a process of recognizing repeated shapes, colors and other visual indicators in images.
Image classification groups images into different categories.
Feature matching is a type of pattern detection that matches similarities in images to help classify them.
Simple applications of computer vision may only use one of these techniques, but more advanced uses, like computer vision for self-driving cars, rely on multiple techniques to accomplish their goal.
Market
The global computer vision market size is expected to reach USD 19.1 billion by 2027, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 7.6% from 2020 to 2027. This technology has emerged as an emulation of a human visual system to support the automation tasks that require visual cognition. However, the process of image deciphering is more complicated than analyzing data in a binary form due to the vast amount of multi-dimensional data in an image for analysis. Artificial neural networks and deep learning are being used to increase computer vision's capabilities of replicating human vision, to address such complexity in developing AI systems to recognize visual data. Besides, this technology has become more adept at pattern recognition than the human visual cognitive system, with the advents in deep learning techniques.
Vision inspection solutions to food and beverages, pharmaceuticals, and automotive industries, provide vision systems for robotic vision and quality control is expected to be prevalent over the projected period
Deep learning algorithms using Convolutional Neural Network (CNN) classifiers allow image classification and object/ pattern recognition, and their segmentation at speed. The development of these AI-powered deep learning systems is anticipated to boost the market growth
Facial recognition and biometric scanning systems in the security and surveillance industry are significantly driving the market growth with the rising use of pattern recognition in high-confidentiality workplaces, such as research labs, nuclear power plants, and bank vaults
Tasks automation in manufacturing units is one of the significant use-cases of computer vision technology as machine vision is highly used for the inspection of manufactured products for non-conformities and defects.
Also, different trends are emerging in the use of computer vision techniques and tools after the COVID-19 outbreaks. It is being used for multiple purposes of fighting against COVID-19, such as medical data monitoring to diagnose patients and movement and traffic control in urban spaces. For instance, Numina, a U.S.-based startup that delivers real-time insights using computer vision for the development of sustainable cities, has developed a tool that enables monitoring of social distancing in the cities, such as New York. Also, the tool built by Numina provides real-time insights on pedestrian movements to monitor how people are following social distancing guidelines (2-meter distance). For another instance, in December 2019, BlueDot, a Canadian start-up that provides an AI platform for infectious disease detection, predicted the coronavirus infections before the statement released by the World Health Organization (WHO) for the pandemic. Therefore, from accelerated drug discovery to social distancing monitoring, AI-enabled with computer vision is at the forefront in the fight against this pandemic.
Artificial intelligence with computer vision technology is becoming increasingly popular in different use-cases, such as imagery solutions in consumer drones and autonomous and semi-autonomous vehicles. Also, the recent advancements in computer vision comprising image sensors, advanced cameras, and deep learning techniques have widened the scope for these systems in various industries, including education, healthcare, robotics, consumer electronics, retail, manufacturing, and security and surveillance, among others. For instance, image captioning in social media platforms is one of the most popular applications of computer vision. These platforms use deep learning algorithms to apply pattern recognition in images shared by the users and provide textual information extracted from the images.