Date: January 1, 2025
Top 12 Practical Applications of Computer Vision: How AI Is Revolutionizing Industries
Computer vision (CV) is a field of artificial intelligence (AI) that enables computers and systems to understand and interpret images and videos. In other words, it is the process by which computers and systems analyze visual data, such as photos or videos, to extract useful information. This allows machines to recognize objects, track movement, analyze scenes and make decisions on this basis. The use of advanced algorithms and models gives computer vision the potential to revolutionize many industries – from medicine, through manufacturing, to agriculture. Discover 12 computer vision examples from the market of the use of CV in various industrial sectors.
Computer Vision Definition
Computer vision is a field of artificial intelligence that allows computers to interpret and analyze visual data such as images, video, camera or sensor signals. The aim of this technology is to automatically understand visual content and make decisions or actions based on it.
The simplest definition of computer vision is that it is the ability of machines to see and understand the world. This is achieved by using machine learning (ML) and deep learning (DL) algorithms that analyze image pixels to extract key information such as objects, colors, shapes or movements, among others. Computer vision is closely related to digital image processing and uses tools such as Python (computer vision Python), libraries (e.g. OpenCV, TensorFlow) and advanced neural network models to achieve high precision in object recognition, image analysis and complex visual operations.
Computer vision is a key element in the development of new technologies, as it allows the automation of many processes that previously required human intervention. Computer vision technology can increase the efficiency of operations in various industries, resulting in time and cost savings, and even improved quality, precision and safety.
The global computer vision market was estimated at $20.31 billion in 2023. It is expected to grow from $25.41 billion in 2024 to $175.72 billion by 2032.
How Does Computer Vision Work?
Computer vision works by processing visual data using advanced algorithms and models. This process can be divided into several key stages:
1. Data Collection – computer vision begins with the collection of visual data using various sensors, such as photo cameras, video cameras, 3D scanners. These devices capture images or videos, which are then analyzed by computer vision systems.
2. Image Processing – the next step is processing the collected images. At this stage, various image processing techniques are used, such as filtering or segmentation, to improve the quality of the input data and prepare it for further analysis.
3. Analysis and Classification – after image processing, data is analyzed using machine learning algorithms. The computer learns to recognize patterns and classify objects based on previously labeled training data.
4. Results Interpretation – the final step is the interpretation of the analysis results. Computer vision systems generate responses based on the identified objects and their context in the environment. For example, autonomous vehicles can determine whether an object is a pedestrian or another vehicle and adjust their actions accordingly. Computer vision technology also uses deep learning techniques, which allow for even more advanced analysis and better understanding of complex visual scenarios. Through all of these processes, computers are becoming increasingly capable of “seeing” and “understanding” the world in a way that is analogous to humans.
Computer Vision – Healthcare
Computer vision is revolutionizing the field of medicine, enabling faster and more accurate diagnostics and more precise treatment. One of the most promising computer vision applications in medicine is diagnostic imaging. Using AI algorithms, doctors can analyze huge amounts of medical data, such as X-rays, CT scans and MRIs, much faster and more accurately than before.
Computer vision algorithms are able to detect even small changes in medical images that may indicate the presence of cancer or help in diagnosing neurodegenerative diseases such as Alzheimer’s disease by analyzing brain images. An example of the use of computer vision in image diagnostics is Google Health, thanks to which doctors can more precisely detect lung cancer, breast cancer, cardiovascular diseases or eye diseases.
Computer vision algorithms and applications also play a key role in robotic surgery. Systems such as the da Vinci use high-resolution cameras to transmit a 3D image of the surgical field to the surgeon’s console. This allows the surgeon to make precise movements even in hard-to-reach areas using surgical tools that are controlled by a computer.
Computer Vision in Retail
Computer vision is also becoming a key element of digital transformation in retail, enabling more personalized shopping experiences and increased operational efficiency.
Computer vision enables monitoring of customer behavior in the store, which allows for a better understanding of their preferences and needs. Computer vision-based systems can analyze customer traffic in the store, identify crowded areas, track customer journeys and measure time spent at individual shelves. This allows for optimization of the store layout, product display improvement and sales increase.
Computer vision technology also enables the creation of heatmaps, which show where customers spend the most time in the store. In this way, it is possible to identify the most attractive places to display products.
A great example of the use of computer vision in retail is Just Walk Out – Amazon’s proprietary technology, used in Amazon Go stores and in selected Amazon Fresh and Whole Foods Market stores. It is possible to make purchases there without having to stand in line at the checkout. Vision systems automatically recognize products that a customer puts in their cart, and payment is automatically charged to the customer’s account.
When it comes to shelf management, computer vision can automate many of these tasks. For example, computer vision systems can automatically check whether the prices on shelves match the prices in the checkout system. They also enable automatic detection of products whose expiration date is approaching, allowing them to be sold as a priority. They can also automate the replenishment of goods.
An example of such use of computer vision in retail is a robot created by Simbe Robotics that autonomously moves around the store, scanning shelves and tracking product locations, inventory levels and prices.
Another example of the use of computer vision in retail is the use of Infrasenses – Pragmile’s proprietary technology, combining computer vision with artificial intelligence – by one of the leading providers of digital television, internet and telephony services for anonymous people counting to determine the level of traffic in the store and to identify the impact of marketing activities on customers’ purchasing decisions.
In addition to analyzing consumer behavior in retail spaces, the solution can also help make data-driven strategic assortment decisions, among other things.
Computer Vision in Agriculture
Computer vision technology is also increasingly used in agriculture, enabling precise control of crops, process optimization, and increased production efficiency, as well as the creation of intelligent and sustainable food production systems.
Computer vision enables monitoring the condition of crops in real time, allowing problems to be detected early and appropriate action to be taken. Computer vision algorithms can analyze images of plants and detect signs of disease, pests, and nutrient deficiencies.
For example, John Deere offers See & Spray™ intelligent spraying technology, which allows farmers to reduce herbicide use by up to two-thirds. This computer vision and machine learning solution uses cameras attached to the sprayer boom to determine what is a weed and what is not.
Harvest automation is another area where computer vision plays a key role, reducing labor costs and increasing efficiency. Agricultural robots equipped with cameras and computer vision algorithms can autonomously identify ripe fruit or vegetables and pick them gently enough not to damage them. For example, Harvest CROO Robotics has created a robot that can pick strawberries more efficiently than humans. The robot uses cameras to identify ripe fruit and gently picks them.
Security and Monitoring – Computer Vision Algorithms and Applications
Computer vision is also playing an increasingly important role in ensuring safety – both in public and private spaces. Advanced algorithms enable monitoring of the environment, detecting potential threats, and responding proactively.
Computer vision-based surveillance systems enable more effective security of facilities and monitoring of large areas. For example, algorithms can analyze the behavior of people in video recordings, detecting suspicious situations such as leaving dangerous objects, intrusion into protected areas, or attempted theft.
Another example is facial recognition technologies – increasingly used in access control systems, enabling the identification of people based on their image. Clearview AI has created a face database that contains billions of publicly available photos downloaded from the Internet. Although this solution is controversial, this system is used by law enforcement agencies in the USA to identify people suspected of committing crimes.
In turn, Clearview Intelligence integrates computer vision and CCTV technology to detect, recognize, and classify objects in real time. This allows for the identification and interpretation of specific events in designated areas, improving safety, management of roads, and public spaces.
For example, the system can detect when a pedestrian crosses the road outside a designated crosswalk, triggering an alert or recording the event for further analysis. It can also identify illegally parked vehicles on sidewalks or in no-entry zones and other traffic violations, and even optimize traffic flow on streets.
Industrial Computer Vision Projects – Computer Vision Technology in Manufacturing and Beyond
Computer vision also plays a key role in modern industry, enabling the automation of many processes, increasing efficiency, improving product quality, and reducing production costs. Thanks to this technology, it is possible to create more flexible and efficient production lines that are able to adapt to changing market requirements.
One of the key computer vision applications in industry is product quality control. Vision systems can detect even the smallest defects (including cracks, scratches, contamination, missing elements) that humans might miss, which results in improved quality of final products and a reduced number of complaints. They are also able to quickly and accurately classify products according to various criteria, such as size, shape, color, or material, as well as verify product dimensions, which is especially important in the production of high-precision elements.
For example, TOMRA Food uses computer vision to extremely accurately sort and classify fruit on production lines – fresh blueberries, apples, and citrus fruits. This technology makes food production more efficient and cost-effective, as it helps minimize losses and maximize packaging results.
In turn, the KiS 4.0 platform, implemented as part of the European Qu4lity project – in which partners such as ABB, Siemens, Philips, and Continental are involved – uses computer vision and artificial intelligence to support the Zero Defect Manufacturing (ZDM) concept in Industry 4.0. Thanks to integration with Industrial Internet of Things (IIoT) technologies, the system enables precise visual analysis of components and immediate response to potential defects, which allows for optimization of production processes.
Computer vision plays a key role in production automation. Industrial robots equipped with cameras and computer vision algorithms are able to perform complex tasks such as assembly, packaging, or palletizing. This technology even enables the creation of fully automated production lines, where robots cooperate with each other and with other machines, performing various tasks continuously.
Vision systems are able to monitor the entire production process, detecting any irregularities and preventing the creation of defective products. For example, FANUC offers a wide range of industrial robots and cobots equipped with advanced vision inspection technology. They are able to support the performance of tasks such as quality control, defect detection, assembly verification, sorting, and picking, increasing the precision, flexibility, efficiency, and safety of production processes in various industries – from the automotive industry to the electronics sector.
Even More Examples of Computer Vision Applications
There are many more computer vision-based projects – from facial recognition systems that improve access control at airports, through analysis of athletes’ movements that increase the effectiveness of training and help them achieve better results, to voice assistants for blind people, using image recognition technology to facilitate movement in public spaces and perform everyday activities.
From applications using augmented reality (AR), thanks to which tourists can discover information about historical places by scanning the monuments around them with their smartphone, to detecting fraud and financial forgery by analyzing signatures, documents, or recordings from surveillance cameras.
Computer vision is a dynamically developing field of artificial intelligence that has the potential to revolutionize many industries, and the prospects for its development are extremely promising. Computer vision not only makes life easier, but also opens up new opportunities for businesses and society, making it one of the most important achievements of modern AI technology.
If you want to learn how computer vision can support your business, schedule a free consultation with Marcin Jabłonowski – Managing Director and AI Solutions Architect at Pragmile.
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