Just as we use our eyes and brains to capture visual information, machines increasingly use cameras and sensors to do the same. This field of artificial intelligence, computer vision, enables software to extract meaningful information from digital images and video, allowing machines to recognize faces, read text, detect objects, analyze movements, measure distances, identify patterns, and interpret visual scenes much like human sight processes the world around us.
This process relies on image acquisition, preprocessing, feature extraction, and machine learning analysis to make decisions and produce an output, such as a self-driving car identifying a stop sign and automatically applying the brakes.
Early work in this area allowed for extracting 3D information about solid objects from 2D photographs and edge detection to identify object boundaries. In 2025, computer vision applications will only expand. For instance, Sam’s Club uses computer vision technology that captures images of carts and verifies payment for all items within a member’s basket to simplify store exits.
In other cases, the photo and file apps across our devices use algorithms to create face models that numerically represent the images of faces and estimate whether different images represent the same face to automatically group pictures of the same people together.
Let’s take a look at some of the benefits and challenges computer vision poses, how it’s used in industries today, and some practical tools you can use to start developing applications.
Key takeaways:
Computer vision applications are pervasive by 2025, including autonomous vehicles (using CV for object and pedestrian detection), healthcare diagnostics (analyzing medical scans for conditions), and retail (powering cashier-less stores and inventory monitoring with image recognition).
Other notable applications involve agriculture (drone-based crop monitoring), security and surveillance (intelligent CCTV with anomaly detection), and augmented reality (where CV helps overlay digital content onto the real world by recognizing surfaces and objects).
These diverse applications showcase how computer vision technology is transforming industries by enabling real-time understanding of visual data, improving safety, efficiency, and user experiences in everyday life.
Organizations across manufacturing, healthcare, retail, and other industries are using computer vision to automate visual tasks, improve decision-making, and handle operations that once required constant human attention. In addition to augmenting human employees’ capabilities, it can also provide benefits such as:
Increased efficiency and automation: The main benefit of computer vision is automating tasks such as inspection, inventory management, or regular monitoring tasks. Automating these tasks with computer vision can complete more analysis over time, increase the amount of data that can be analyzed, and provide real-time analytics as it scans and processes data. Computer vision systems can also process more items per minute than the human eye, which means certain applications will yield more efficiency.
Better safety: Using computer vision applications and systems, especially in highly industrial environments, increases safety as it reduces the need for human intervention. Instead, employees can use computer vision applications and hardware to inspect potentially hazardous materials or work on machines that potentially cause injury.
Boosted accuracy: While the human eye can detect a lot, there are still certain components it might not be able to detect regularly or can easily miss. Computer vision applications rely on high-resolution cameras and sensors that make it much easier to detect errors at a more consistent rate and at a much higher resolution, which results in greater task accuracy over time.
Setting up and maintaining computer vision applications and systems does require work, proper planning, and regular data maintenance. Some considerations and challenges include
Poor data labeling: AI applications like computer vision rely heavily on accurate, high-quality data that’s labelled and annotated to train the system and help it learn over time. Poorly labeled, biased, or incomplete data will result in limited detection capabilities, increased false positives, and unreliable applications.
Poor data quality: Computer vision models are only as useful as the data they can access. Poor data is incomplete, irrelevant, duplicated, or biased. These models will have reduced accuracy, data and privacy leaks, model hallucinations, or skewed predictions.
Retraining models: To remain effective and address new tasks, computer vision models must be able to process new data and have regular training. This requires you to have dedicated staff to source data, complete any necessary pre-processing tasks, effectively label, and run training workflows to ensure that any new data improves the computer vision applications instead of flooding the model with bad data.
Model complexity: Computer vision applications use AI and machine learning, which require special expertise and knowledge of how to set up and maintain large datasets that help extract information from images with repeatable accuracy over time. These datasets can quickly grow, requiring significant computing resources as well as processes for data annotation and collection.
Computer vision is used in agriculture, automotive, finance, oil and gas, manufacturing, and retail sectors for all types of projects. Given its benefits of improved accuracy, effective automation, and reduced human error, its applications are only growing, and the industry is expected to grow at a 19.8% CAGR from 2025 to 2030. Here’s a look at some of the main ways you can see computer vision in action:
Computer vision, along with cameras, IoT devices, and drones, is helping agriculture modernize practices and farmers maximize outputs while monitoring costs. Using the data collected from these systems, farmers now have a better understanding of their operations, available resources, crop health, and farming methods.
Livestock monitoring is also a big use case for computer vision in agriculture. Having systems in place that can monitor animal health, behavior, and temperature makes it much easier to evaluate livestock regularly and take preventative measures before it’s too late. Autonomous livestock monitoring systems, such as CattleEye, rely on AI and computer vision to track how cattle walk and behave, which can indicate health or mobility issues.
Augmented reality has emerged in both entertainment and industrial environments. This technology relies on computer vision applications to help with spatial mapping, motion tracking, object detection and recognition, surface detection, and occlusion handling. The augmented reality application will overlay graphics over the real-world environment to provide information to the user, such as game graphics or machine part status.
A prime example of this is Pokémon GO, which relies on the user’s smartphone camera, gyroscope, clock, and GPS to locate and capture Pokémon based on their location and time of day. Augmented reality, along with computer vision technology, can display Pokémon as if they exist right in front of the user, and projected commands illustrate gameplay actions.
This type of computer vision uses images to either identify, group, or verify a person’s identity. The application will look for key facial features or distances between facial features and then compare them against a template or facial capture in a database. This use case is mainly seen for identity verification, personal security, event monitoring, or smart home applications.
Despite the technology’s controversy regarding data privacy, personal privacy, and data biases, it does provide high levels of accuracy, with a single scan detecting someone’s face at 99.5% or better, according to the NIST. This opens the door for a variety of security applications and allows for using facial recognition as another form of biometric authentication, in addition to fingerprints and irises.
Finance is a data-heavy industry that can rely on computer vision applications for expedited document processing and analysis, customer identification and recognition, regulatory compliance and surveillance, anomaly detection, visual fraud detection, and ATM and in-branch security. Using computer vision for these applications, along with AI, helps financial institutions process a large amount of data very quickly and efficiently gain insights about customer behavior and transaction patterns.
Ultralytic’s YOLO11 computer vision model can support finance use cases such as line monitoring, credit risk assessment, document processing, object counting, and image segmentation.
Computer vision is widely used in the manufacturing sector, particularly for quality inspection, supply chain optimization, workforce safety, automated product assembly, and barcode scanning. These uses can help manufacturing organizations increase productivity, reduce human error, improve safety measures, and reduce product defects.
For example, Inspekto from Siemens provides an out-of-the-box system that can provide over 100 inspections per minute and provides continuous optimization.
Using computer vision for medical imaging is one example of how technology can augment professional expertise. Doctors, nurses, and technicians can use images from MRIs, ultrasounds, X-rays, and CT scans to detect potential issues or health problems, but computer vision can provide more detailed images, detect image abnormalities, and aid in early disease detection. The main benefit of using computer vision for certain tasks is that it can potentially detect abnormalities that might go undetected by the human eye.
For instance, AI tool Mia (which stands for mammography intelligent assessment), developed by Kheiron Medical Technologies and Imperial College London, detects up to 13% more breast cancers in screenings than human radiologists alone.
Computer vision applications are used in military and defense for missile vision systems, unmanned autonomous vehicles (UAVs), battlefield object recognition, remote-controlled semi-automatic vehicles, threat recognition, and infrastructure analysis. This helps personnel evaluate situations from afar, scan unfamiliar or challenging terrain, and make crucial decisions on how to address potentially dangerous situations.
It can also be used for gesture control, which enables pilots to interact with plane dashboards and cockpit mechanisms through vision and hand gestures. The CSEM Pilot Eye Gaze and Gesture tracking for Avionics Systems using Unobtrusive Solutions (PEGASUS) system helps improve overall pilot experience, situational awareness, and decision making when interacting with cockpit controls.
The oil and gas sector uses technology to help detect potential drilling sites, collect natural resources, and perform environmental analysis. Computer vision applications provide an additional layer of safety and accuracy as they allow professionals to perform remote pipeline inspection, oil and gas field monitoring, predictive maintenance, leak and corrosion detection, and fire detection.
ModuSpec provides real-time subsea asset monitoring for blowout preventers, mechanical devices that keep oil and gas wells secure and prevent uncontrollable leaks. It can use computer image systems to help gauge health and real-time analytics to provide maintenance feedback and system insights.
In the retail sector, computer vision can benefit both consumers and businesses. Equipped systems can monitor shelf inventory, identify out-of-stock items, collect data for product loss and prevention, and personalize store experiences.
Computer vision is being used to map out store layouts based on actual customer behavior instead of reactive plans from retailers. Using both computer vision and AI, along with collected data from store surveillance, the system can detect shopper patterns, how they travel through the store, and which sections they stop at the most, and figure out an optimal layout.
Computer vision applications allow warehouse robots to become more aware of their surroundings and complete automated tasks such as object detection and recognition, quality control inspection, autonomous vehicle navigation, terrain mapping, inventory management, and warehouse operations. In using computer vision, these robots can process real-time data, understand their surroundings, and work alongside humans.
A primary example of this is Amazon’s Proteus, the company’s fully autonomous warehouse robot that relies on cameras and sensors to navigate fulfillment sites and move large carts filled with packages. As these carts can weigh up to 800 pounds, using a robot for this task reduces the risk of human injury and moves packages more quickly through the warehouses, reducing fulfillment time.
Developing computer vision applications requires a mix of software libraries, frameworks, and platforms. Your tech stack will depend on what specific use case you’re developing for, but there are a few main tools that you can use to get started:
OpenCV is an open-source library that provides access to more than 2,500 machine learning and computer vision algorithms. As the de facto tool for image processing, it provides algorithms useful for facial detection, 3D model extraction, high-resolution image stitching, and object tracking. It has a large community that offers support, and you are free to change the available algorithms to suit your specific needs. It is compatible with C++, Python, Java, and MATLAB.
TensorFlow provides tools, resources, and libraries for machine-learning-based computer vision applications. It can easily build models for image classification, object detection, and facial recognition. It also offers TensorFlow Light, which is suited for on-device machine learning with edge devices and is compatible with Python, C, C++, Java, and JavaScript.
Compute Unified Device Architecture (CUDA) from NVIDIA is a parallel computing platform and API that lets you use GPUs to run computer vision applications faster. The toolkit supports image processing, video processing, and signal processing functions for computer vision. It can be used for image manipulation, 3D graphics rendering, facial recognition, and real-time image processing. The platform is compatible with Python, C, C++, Fortran, and MATLAB.
PyTorch is an open-source library operating under the BSD-3 license. Originally developed at Meta, it is suitable for image estimation models, image classification, and image segmentation. It offers deep neural networks built on an automatic differentiation system and Tensor computing, parallel processing, and a large repository of pre-trained models for developers to use. It is built with Python, C++, and CUDA.
Roboflow is an end-to-end computer vision platform that helps developers build, train, and deploy models across multiple industries. It has features for dataset management, AI data annotation tools, in-platform model training, deployment support, and access to over 7,000 pre-trained models within its Roboflow Universe. It is built on OpenCV and supports Python and offers a REST API for working with other programming languages.
Vision Transformers (ViTs): Computer Vision with Transformer Models
An Introduction to Computer Vision in JavaScript using OpenCV.js
What is computer vision in simple terms?
Computer vision is the use of artificial intelligence to help machines understand the world based on visual inputs, such as photos or videos. Systems that use computer vision rely on sensors and cameras to try and capture information just as the human brain would with eyes and ears.
What are the main benefits of computer vision applications? Computer vision provides increased efficiency through automating tasks like inspection and inventory management, completing more analysis over time with real-time analytics. It also improves safety by reducing human intervention in hazardous environments and boosts accuracy through high-resolution cameras and sensors that detect errors more consistently than the human eye.
What are the main challenges in implementing computer vision systems? Poor data labeling is a significant challenge, as computer vision relies heavily on accurate, high-quality data that’s properly labeled and annotated for training. Poor data quality including incomplete, irrelevant, duplicated, or biased data results in limited detection capabilities, increased false positives, and unreliable applications.
What industries use computer vision the most?
Computer vision has a wide range of use cases. It is highly used in healthcare, manufacturing, automotive, agriculture, and retail.
How does computer vision differ from image processing?
Computer vision extracts information from images and performs tasks. Image processing is a subset of computer vision that is focused on preparing images (via sharpening and smoothing) and videos so that machine learning algorithms can extract more information.
What tools are commonly used for computer vision development?
Top tools for computer vision include OpenCV, TensorFlow, NVIDIA CUDA, and MATLAB.
Is computer vision AI or machine learning?
It is both. Computer vision is a field of AI that uses machine learning and neural networks to function.
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