Vision is the biggest gift given to humans. As we continue to struggle towards making technology more and more like us, this is one thing we need to put the most effort into. Machines are now easily able to capture images, but recognizing the surrounding environment and objects cannot be done if they don’t let how to interpret the information that lies in them. That’s why Computer Vision is important if we want to make humans truly intelligent. Let’s see what it is and how it is making different fields better.
What is Computer Vision?
As we discussed in our previous post, Artificial Neural Networks (ANNs) interpret data by processing it repeatedly and drawing useful conclusions. This property of Deep Learning helps computers “see” images and learn what is what in them.
Formally, Computer Vision is defined as “Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.”
Computer Vision algorithms read images and match them with existing data just like any other deep learning algorithm to learn how to classify different things, and a machine can do it with much better results in terms of accuracy and efficiency. Let’s see how the term came into being and how much it has progressed to date.
A Brief History
Even though early computer vision experiments started in the 1950s and were first used commercially to differentiate between typed and handwritten text in the 1970s, computer vision applications today have grown exponentially. By 2022 the demand for computer vision and hardware is expected to reach $48.6 billion. Today, the growing popularity of mobile cameras means a large data of photos and videos, and the ease of accessibility to computer vision makes it even more appealing to businesses. Accuracy rates for object recognition and classification have risen from 50 percent to 99 percent in under a decade, making today’s systems even more accurate than humans.
Applications of Computer Vision
We have seen some exciting inventions in recent years that are all possible thanks to computer vision. We will discuss some of the applications of Computer vision that make it a field worth the effort.
Computer Vision is a vitally enabling technology for modern armies, which helps security systems detect enemy troops and enhances the aiming ability of guided missile systems. Military related things such as spatial awareness and reaction rely heavily on image sensors to provide intelligence used for tactical decision-making on the battlefield. Another critical application of Computer Vision is in the areas of autonomous vehicles, which need to navigate challenging terrain and detect opponents. Computer Vision systems also allow human drivers and pilots to escape enemy fire. As with many military inventions, the incorporation of technology is now affecting a wide range of sectors.
Computer vision has also caused a splash in the retail sector. On 22 January this year, Amazon Go store opened its doors to customers in various locations. It is a partially automated store that does not have any checkout stations or cashiers. Customers can exit the store with products of their choice by using computer vision, deep learning, and sensor fusion, and get charged for their purchases via their Amazon account. The technology is not yet 100 percent perfect, as several official store technology tests have shown that some items have been left out of the final bill. It’s one remarkable step in the right direction, though!
Computer vision is responsible for all the hype around Self-driving cars these days. Tesla, Uber, BMW, Volvo, Audi, even Google, and many other brands are going into this business. Manufacturers use multiple cameras, lidar, radar, and ultrasonic sensors to collect environmentally friendly images so that their self-driving cars can track obstacles, lane markings, and see traffic signals to drive safely. Although not yet at the point of completely replacing human drivers, autonomous vehicle technology has significantly advanced in recent years.
As 90 percent of all medical data is focused on imaging, there are plenty of use cases in medicine for computer vision. By allowing new medical diagnostic approaches to examine X-rays, mammography, and other scans, we can use them to identify issues sooner and help with the surgery. It can also be of great help to medical staff by speeding up their work. Intelligent systems can analyze images and diagnose patients with much better accuracy than humans, once fully trained.
The major advantage of computer vision is the high precision with which it can replace human vision. There are several processes that people do today, which can be replaced due to this technology. We are still suffering from some big challenges apart from the great benefits computer vision algorithms pose today. The first is the lack of well-captured images to train the algorithms for optimal performance, and the second is the lack of precision when applied to real-world images that differ from the training dataset. But they are becoming increasingly efficient, and it is only a matter of time before they reach their true potential.