Computer vision has become a captivating domain of inquiry, with its technological breakthroughs paving the way for significant transformations across various industries. Essentially, it entails the process of training machines to decipher visual data from their environment, much akin to how humans perceive things. This cutting-edge discipline empowers computing systems to identify, monitor, and trace objects, comprehend physical movements, discern human faces, and even comprehend emotional cues. In this article, we will explore some of the most significant computer vision examples and how they are transforming our daily lives.
Computer vision technologies help a robocar to distinguish a lamppost from a deer, and a courier robot not to confuse a lawn with a sidewalk.
What is Computer Vision and how does it work?
They also allow you to recognize a person in a crowd, moderate content on social networks, diagnose an illness from an xray, distinguish a cat from a dog, and a Corgi from a Labrador.
Computer vision (Computer Vision, CV) is a field of artificial intelligence related to the analysis, classification and recognition of images and videos. CV systems are usually based on algorithms based on machine learning – with their help, they learn to distinguish one object from another, to see patterns and patterns. A person learns to recognize images in the process of getting to know the world around him – even as a child, he remembers how a cat differs from a dog, and the Among Us interface from the Minecraft interface. The computer “thinks” differently – in order to teach the system to recognize images, it needs to “feed” a dataset with marked data, which clearly shows the differences between one object and another.
Data markup in computer vision
Data markup, by the way, is a whole science – and quite laborious. Usually, freelancers are hired for this, who remotely label videos and images. The more accurate the markup and the more data, the more accurate the computer vision system will work. At the same time, a lot depends on the specifics – an algorithm that is oriented in different types of birds will not help a robotic vehicle to recognize an obstacle on the road. Although there are interesting precedents. For example, in Japan, an algorithm for recognizing pastries has been used to diagnose cancer.
Сomputer vision technology is not perfect
Every year the technology evolves, but it still cannot do without glitches: algorithms confuse people with animals, take abstract patterns for real objects, and sometimes they cannot distinguish a turtle from a gun. The task of computer vision specialists is to minimize such incidents and teach algorithms to confidently navigate the world around them. For examples, in production, the system will be able to detect a defective product, in a clinical it will help a doctor to distinguish a malignant tumor from a benign one, and astrophysicists will be able to quickly classify celestial bodies.
The technology itself is neutral, but it can be used in many ways – for example, some states use it to deanonymize protesters. Therefore, AI professionals are encouraged to study ethics in the field of machine learning. We specifically included one of these courses in this selection. Further in the article you will find real computer vision examples and their application in real life.
Additional Information and Research
Computer vision has been a topic of extensive research, with numerous studies and advancements being made in recent years. Here are some additional facts and research to support the computer vision examples mentioned in the article:
- Self-Driving Cars. According to a report by MarketsandMarkets, the global self-driving car market is expected to grow from $24.1 billion in 2021 to $126.8 billion by 2030, at a compound annual growth rate (CAGR) of 19.5% during the forecast period. Additionally, a study by the University of Michigan Transportation Research Institute found that self-driving cars could reduce traffic fatalities by up to 90%.
- Facial Recognition. Facial recognition technology has been met with both praise and criticism. Some experts believe it has the potential to improve security and streamline various processes, while others are concerned about its potential misuse and invasion of privacy. In 2020, the European Union proposed a temporary ban on facial recognition in public places to allow time for policymakers to assess the technology’s impact.
- Object Detection. In the healthcare industry, object detection has been used to monitor patient movements and improve their care. A study published in the Journal of Medical Internet Research found that using machine learning-based object detection technology in hospital rooms could improve hand hygiene compliance among healthcare workers.
- Augmented Reality. According to a report by Zion Market Research, the global augmented reality market is expected to reach $92.7 billion by 2026, at a CAGR of 43.8% during the forecast period. Additionally, augmented reality has been used in various industries, including gaming, education, and advertising. For instance, IKEA’s augmented reality app allows customers to visualize furniture in their homes before making a purchase.
- Deep Learning. Deep learning has been a game-changer in machine vision, enabling machines to process vast amounts of visual data more efficiently. In 2021, OpenAI released DALL·E, a deep learning-based AI model that generates images from textual descriptions. The model has been widely praised for its ability to create highly detailed and realistic images.
- 3D Computer Vision. In the construction industry, 3D computer vision has been used to improve safety and efficiency. A study published in the Journal of Construction Engineering and Management found that using 3D computer vision technology in construction sites could improve safety by reducing accidents caused by workers not wearing personal protective equipment.
- Generative Adversarial Networks (GANs). GANs have been used in various applications, including creating deepfakes and generating realistic images and videos. However, there are concerns about the potential misuse of this technology. In 2019, Google released a dataset of synthesized speech created using GANs to help improve the technology’s accuracy and reliability.
Computer Vision Examples
One of the most exciting applications of computer vision examples is in the field of autonomous vehicles. With the help of computer vision algorithms, self-driving cars can navigate roads and traffic with ease. Machine vision enables the vehicle to “see” its surroundings, detect obstacles, and make real-time decisions to ensure safe navigation. Companies like Tesla, Google, and Uber are already investing heavily in this technology, and it is predicted that autonomous vehicles will become a common sight on our roads in the near future.
Another significant application of computer vision is in the field of medical imaging. Computer vision algorithms can analyze medical images such as X-rays, CT scans, and MRIs to detect abnormalities and diagnose diseases. This technology can also be used to monitor patients and track the progress of their treatment. For instance, a study published in the Journal of Digital Imaging found that a computer vision algorithm could accurately detect and classify diabetic retinopathy in retinal images, potentially reducing the need for manual grading by doctors.
How Computer Vision Examples are Revolutionizing Industry
As mentioned earlier, ethical considerations must be addressed when it comes to the use of computer vision technology. Facial recognition technology, in particular, has come under scrutiny for perpetuating bias and discrimination. In 2018, a study conducted by researchers at MIT and Stanford found that three commercially available facial recognition systems had higher error rates for darker-skinned individuals and women, indicating a bias in the algorithms. Such findings highlight the need for ethical guidelines and regulations to ensure that machine vision is used responsibly and without discrimination.
Computer vision can help improve overall efficiency
Computer vision is also being used to improve the efficiency and accuracy of manufacturing processes. For instance, computer vision can be used to monitor assembly lines and detect defects or errors in products. This can help reduce waste, improve quality control, and enhance overall efficiency. Additionally, computer vision can be used to identify patterns in data, enabling companies to make more informed decisions about production and supply chain management.
Future Developments in the field of computer vision
Advancements in Deep Learning and Neural Networks
Recent advances in deep learning and neural networks, which are computational models inspired by the structure and function of the human brain, have significantly improved the accuracy and efficiency of machine vision algorithms. For examples, a study published in Nature in 2020 showed that a neural network-based approach to image recognition outperformed traditional methods by a wide margin. These advancements have the potential to further enhance the capabilities of computer vision and make it more useful in various fields.
Integration with Other Technologies
Machine vision is being combined with other technologies, like augmented reality and virtual reality, to create new possibilities for gaming, education, and healthcare. This integration allows for the creation of more interactive and immersive virtual environments that can respond to the user’s actions and movements. Moreover, machine vision can be utilized in healthcare to enhance the accuracy and efficiency of medical procedures by enabling surgeons to visualize internal organs and structures in real-time.
Expansion of IoT and Smart Cities
The fields of the Internet of Things (IoT) and smart cities are rapidly expanding and require the collection and analysis of vast amounts of data. Machine vision can have a significant impact in these areas by enabling real-time analysis of visual data. For example, computer vision can be used to monitor traffic patterns and optimize transportation routes in smart cities. Additionally, computer vision can be used to detect and respond to emergencies in real-time, improving public safety and security. In fact, there can be a lot of examples
Impact on eCommerce and Retail
The fields of the Internet of Things (IoT) and smart cities are rapidly expanding and require the collection and analysis of vast amounts of data. Computer vision can have a significant impact in these areas by enabling real-time analysis of visual data. For example, machine vision can be used to monitor traffic patterns and optimize transportation routes in smart cities. Additionally, computer vision can be used to detect and respond to emergencies in real-time, improving public safety and security.
In conclusion, machine vision is a rapidly evolving field that is finding new applications across a wide range of industries. From self-driving cars and security systems to medical imaging and eCommerce, computer vision is making our lives easier, safer, and more efficient. This article has explored several examples of computer vision in action, highlighting the real-world impact of this technology has on our present and future.
We can expect even more exciting innovations and applications
As advancements in deep learning, integration with other technologies, IoT, and smart cities continue to drive the development of computer vision, we can expect even more exciting innovations and applications in the future. However, it is important to approach this technology with caution and address ethical considerations to ensure that its benefits are realized without sacrificing privacy or safety.
Overall, these computer vision technology examples demonstrate the potential of this technology to transform our world in meaningful ways, and we can look forward to continued growth and development in the years to come.
Using these computer vision examples, we can give ourselves a general understanding and direction of how this industry will develop in the future. We recommend that you delve into this topic and keep your finger on the pulse if you want to be part of what makes this world more technologically convenient and functional.