XenonStack Recommends

Enterprise AI

Top 6 Computer Vision Applications | 2023

Dr. Jagreet Kaur Gill | 27 April 2023

Computer Vision Applications

What is Computer Vision?

It is a branch of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital photos, videos, and other visual inputs — and then act or recommend on that information. If artificial intelligence allows computers to think, it allows them to see, watch, and comprehend.

How does Computer Vision work?

A large amount of data is required for it. It repeatedly executes data analysis until it detects distinctions and, eventually, identifies pictures. To teach a computer to detect automotive tyres, for example, massive amounts of tyre photos and tire-related materials must be given to it in order for it to understand the distinctions and recognize a tyre, especially one with no faults.

Face Recognition uses computer algorithms to find specific details about a person's face. Click to explore about our, Face Recognition and Detection

Applications of Computer Vision

Some of the most popular computer vision applications in the industry are listed below:

How is computer vision used in transportation?

The growing need for transportation has accelerated technical progress in this business, with it at the forefront.

Intelligent Transportation System (ITS) has become a vital sector for advancing transportation efficiency, efficacy, and safety, from autonomous cars to parking occupancy detection.

This industry's most popular computer vision applications.

Self-driving cars

It is used to recognize and categorize things (such as road signs or traffic signals), construct 3D maps, and estimate motion, and has played an essential part in making self-driving cars a reality.

Sensors and cameras in self-driving cars gather data about their environment, analyze it, and respond appropriately.

Pedestrian detection

It employs cameras to recognize and detect pedestrians in photos or videos while considering variables such as body clothing and posture, occlusion, illuminance in various settings, and background clutter.

Parking occupancy detection

In Parking Guidance and Information (PGI) systems, it is already commonly utilized for visual parking lot occupancy detection. It's a cheaper alternative to more expensive sensor-based systems that require routine maintenance.

Traffic flow analysis

Drone and camera-based traffic flow tracking and estimating are now possible because of advances in it.

Road condition monitoring

Automated Pavement Distress (PD) detection effectively enhances road maintenance allocation efficiency and lowers the safety risk associated with accidents.

AI-based Video Analytics's primary goal is to detect temporal and spatial events in videos automatically.Click to explore about our, AI-based Video Analytics

What are the applications of computer vision in Healthcare?

One of the most valuable sources of information is medical imaging data.
The most prominent medical computer vision applications.

X-Ray analysis

It may be used successfully in the context of medical X-ray imaging for treatment and research, MRI reconstruction, and surgical planning.

Although most clinicians still use manual X-ray image analysis to diagnose and treat ailments, it can automate the process, enhancing efficiency and accuracy.

CT and MRI

It can help doctors spot cancers, internal bleeding, blocked blood arteries, and other life-threatening illnesses by analyzing CT and MRI data. The process's automation has also improved accuracy since robots can now recognize nuances unseen to the human eye.

Cancer detection

Clinicians can spot abnormalities and alterations by comparing diseased and non-cancerous cells in photographs.

Automated detection allows for a speedier cancer diagnosis using data from magnetic resonance imaging (MRI) scans. It is already being used to identify breast and skin cancer.

Blood loss measurement

Postpartum hemorrhage is one of the leading causes of death during delivery. Until recently, doctors could only approximate how much blood a patient had lost after delivery.

Thus, it enabled more precise blood loss assessment, allowing medical practitioners to treat patients more effectively.

Digital pathology

  • It is employed in image processing and interpretation.
  • Extensive examinations of sample tissues
  • Pathology categories that correspond to previous cases
  • Accurate diagnosis and early detection
Text Analytics is the method of transforming unstructured text data into significant data for analysis.Click to explore about our, Text Analytics Techniques


It aids in the automation of quality control, reducing safety hazards, and developing production efficiency.

Applications of it in the industrial industry

Defect inspection

  • Large-scale manufacturing plants frequently fail to attain 100% accuracy in fault identification in their finished items.
  • Camera-based systems may gather real-time data, evaluate it, and compare the findings to preset quality criteria using it and machine learning algorithms.

Reading text and barcodes.

Because most items include barcodes on their packaging, a technology known as OCR can successfully identify, validate, convert, and translate barcodes into legible text.

Product assembly

World-class enterprises, such as Tesla, have already automated their product assembly lines—the company claimed to automate more than 70% of its production operations.

It creates 3D modeling designs, leads robots and humans, recognizes and monitors product components, and aids in the maintenance of packaging standards.


The construction industry is adopting technology for PPE identification, infrastructure asset inspection, workplace hazard detection, and predictive maintenance. The most common applications in construction.

Predictive maintenance

Computer vision-powered devices use cameras to detect flaws and changes in machines' incoming data. They signal the system when they discover an issue, allowing human operators to take corrective action before an asset is harmed or an accident happens.

Computer vision makes better decisions faster, more efficiently, and more honestly than people can consistently do.Download to explore the potential of Computer Vision

How computer vision is used in agriculture?

Artificial intelligence models have significantly contributed to the agricultural industry in crop and yield monitoring, automated harvesting, weather conditions analytics, animal health monitoring, and plant disease diagnosis.

Agriculture computer vision use cases

Crop and yield monitoring

It enables continuous real-time monitoring of plant development and identifying agricultural alterations caused by malnutrition or disease.

Automatic weeding

When compared to automated solutions, human labor is both expensive and inefficient. Furthermore, typical weeding methods include spraying pesticides and frequently contaminating nearby healthy plants, water, or animals.

Insect detection

Early insect pest identification allows farmers to take proper precautions to protect their crops and limit the damage.

Livestock health monitoring

  • Computer vision systems aid in the remote and real-time monitoring of animals and their behaviors.
  • It is beneficial for counting animals, identifying sickness or aberrant behavior, and tracking birthing.

Plant disease detection

  • It is also frequently utilized for the automated identification of plant diseases, which is especially important in the early stages of plant development.
  • Deep learning systems employ picture data to diagnose illnesses, assess severity, and anticipate affect yield.

Aerial survey and imaging

  • Drone-based scene and environment monitoring has become a crucial component of agricultural transformation.
  • Farmers may now use visual data taken by drone cameras and processed it to remotely monitor crops and livestock and collect information on field topography and soil composition.

Irrigation management

Drones and cameras can collect data that can be used to analyze plant health and soil composition.

A low cost, accurate analysis of live videos using Open Source Big Data Technologies including OpenCV, Apache Kafka, Apache Spark. Taken From Article, Cloud Video Analytics and Surveillance

What are the applications of computer vision in retail?

Cameras installed in retail outlets enable merchants to capture significant amounts of visual data that can be used to improve the consumer and team member experience.

The development of its systems for processing this data makes the digital transformation of the actual world much more feasible.

The retail industry's computer vision applications.


Autonomous check-out is now feasible owing to computer vision-based systems analyzing consumer interactions and tracking goods movement.

Automatic replenishment

  • Its systems may also acquire picture data and do a thorough inventory scan by tracking things on shelves at millisecond intervals.
  • The technology may send out rapid information about stock-outs and sales and assist workers with inventory management.

Intelligent video analytics

  • AI-powered systems may detect suspicious activity and inform employees, who can immediately analyze it and take the necessary action by analyzing CCTV camera video.
  • Computer vision-based theft detection systems have shown to be more accurate since they allow for real-time movement analysis. AI-powered cameras can also identify other actions, such as physical attacks or accidents, boosting safety.

Foot traffic and people counting

  • In-store cameras can readily gather its systems' image or video data to recognize and count persons entering and exiting the business.
  • It is especially effective during COVID-19, when social distance regulations are imposed, and only a restricted number of people are permitted in a business at once.
Our solutions cater to diverse industries with a focus on serving ever-changing marketing needs.Click here for our Computer Vision Services and Solutions


Computer vision may be used successfully in various businesses that rely on image and video data. It enables us to automate routine processes, enhance diagnostic accuracy, increase agricultural output, and maintain safety. We can anticipate it to continue being the driving force that transforms businesses of all types as more organizations adopt the AI-first strategy.