27+ Most Popular Computer Vision Applications and Use Cases in 2024

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Transportation

The increasing demands of the transportation sector have propelled technological development in this industry, with computer vision at its center.

From autonomous vehicles to parking occupancy detection, the Intelligent Transportation System (ITS) has become a critical field for promoting the efficiency, effectiveness, and safety of transportation.

Let’s have a look at some of the most popular computer vision applications in this industry.

Self-driving cars

In 2023 autonomous vehicles are no longer science fiction. In fact—

Thousands of engineers and developers worldwide are already testing and improving the reliability and safety of self-driving cars. 

Computer vision is used to detect and classify objects (e.g., road signs or traffic lights), create 3D maps or motion estimation, and played a key role in making autonomous vehicles a reality.

Self-driving cars collect data on their surroundings from sensors and cameras, interpret it, and respond accordingly.

Researches working on the ADAS technology combine computer vision techniques such as pattern recognition, feature extraction, object tracking, and 3D vision to develop real-time algorithms that assist driving activity.

Here's how AI traffic light detection works in real life.

Pedestrian detection

Pedestrian detection and tracking have become a vital computer vision research area because of its potential impact on the design of pedestrian protection systems and smart cities.

It uses cameras to automatically identify and locate the pedestrians in images or videos while considering variations related to body attire and position, occlusion, illuminance in different scenarios, and background clutter.

The detection of pedestrians finds applications in fields such as autonomous driving, traffic management, and transit safety and efficiency.

Looking for the perfect bounding box tool? Check out 9 Essential Features for a Bounding Box Annotation Tool before you make a decision.

Parking occupancy detection

Computer vision is already widely used for visual parking lot occupancy detection in Parking Guidance and Information (PGI) systems. It’s an alternative to more costly sensor-based technologies that require regular maintenance.

Thanks to the development of CNNs, camera-based parking occupancy detection systems have quickly achieved extremely high accuracy, almost unsusceptible to changes in illuminance and weather conditions.

Parking occupancy detection can also be combined with License Plate Recognition to keep track of which car is occupying which slot at any given time. 

PS. Here are some datasets you can use for training your parking lot detection models:

• PKLot

• CNRPark-EXT

Traffic flow analysis

Drone and camera-based traffic flow tracking and estimation have also been made possible thanks to the developments in the field of computer vision.

The algorithms can now accurately track and count freeway traffic or monitor and analyze traffic density in urban areas (e.g., at intersections), helping to design better traffic management systems and improve road safety.

Read Best Practices for Annotating with Bounding Boxes to improve your annotations for object detection models.

Road condition monitoring

Computer vision has also been successfully used for defect detection and found application in assessing the infrastructure condition by monitoring changes in concrete and asphalt.

Automated Pavement Distress (PD) detection has proven successful in increasing road maintenance allocation efficiency and decreasing the safety risk related to accidents.

Computer vision algorithms use the collected image data that is later processed to build automatic crack detection and classification systems enabling human-independent targeted rehabilitation and preventive maintenance.

Healthcare

Medical imaging data is one of the richest sources of information. But, here's the caveat—

Without the right technology in place, doctors are forced to spend hours manually analyzing patient data and doing administrative work.

Luckily, as the years went by and the technology advanced, the healthcare industry had become one of the fastest at adopting new automation solutions, including computer vision.

Let’s explore some of the most popular computer vision applications in healthcare.

X-Ray analysis

In the context of medical X-ray imaging, computer vision can be successfully applied to treatment and research, MRI reconstruction, or surgeries planning. 

Although most doctors are still relying on the manual analysis of X-ray images for diagnosing and treating diseases, computer vision can automate the process, increasing both efficiency and accuracy.

State-of-the-art image recognition algorithms can pick up patterns in X-ray images too subtle for the human eye.

Have a look at how V7 handles X-Ray image annotation.

CT and MRI

Computer vision is also widely applied in the analysis of CT scans and MRI.

From designing AI systems to analyze radiology images with the same levels of accuracy as human doctors (while reducing the disease detection time) to deep learning algorithms that increase the resolution of MRI images—computer vision is the key to improving patient outcomes.

Here's an example of an ML-assisted brain and spinal cord annotation.

Using computer vision to analyze CT and MRI scans can help doctors to detect tumours, internal bleeding, clogged blood vessels, and other life-threatening conditions. The automation of the process has also proven to increase the accuracy because the machines can now identify details invisible to the human eye.

Check out V7 Medical Image Annotation for more details.

Cancer detection

Image recognition allows doctors to identify anomalies and changes by comparing cancerous and non-cancerous cells in images.

Automated detection allows for a faster cancer diagnosis using data from magnetic resonance imaging (MRI) scans. Computer vision is already successfully applied in breast and skin cancer detection. 

Here’s an example of annotated moles that can be analyzed to detect melanoma skin cancer in its early stages.

Blood loss measurement

Postpartum hemorrhaging is one of the biggest causes of mortality in childbirth. Traditionally,  physicians could only guess how much blood a patient has lost during childbirth.

Thanks to computer vision, doctors can now measure blood loss during childbirth by using an AI-powered tool to analyze images (taken with an iPad) of surgical sponges and suction canisters. 

The Orlando Health Winnie Palmer Hospital for Women and Babies was one of the facilities that implemented this technology. It turned out that in the majority of cases, doctors tend to overestimate the amount of blood lost by patients during child delivery. 

Computer vision thus allowed for a more accurate blood loss measurement that helps medical professionals to treat patients more efficiently.

Check out 21+ Best Healthcare Datasets for Computer Vision to find open-source medical datasets for your projects.

Digital pathology

Thanks to the proliferation of whole-slide-imaging (WSI) digital scanners, computer vision can now process medical image data to detect and identify the type of pathology on display.

It is used for: 

• Image analysis and interpretation 

• Detailed inspections of sample tissues 

• Pathology types matching to earlier cases

• Diagnosis accuracy and early detection 

Computer vision-enhanced digital pathology improved diagnostic accuracy and efficiency, helping doctors save precious time and make better-informed decisions.

Movement analysis

Pose estimation has been used to help analyze patient movement and assist doctors in diagnosing neurological and musculoskeletal diseases.

Although a vast majority of approaches to human pose estimation focus on adults, this computer vision technique has also been applied for Medical Infant Motion Analysis. 

By monitoring and assessing an infant’s spontaneous moves, doctors can predict neurodevelopmental disorders at a very young age and take appropriate measures. An automated motion analysis system can capture infant body movements and spot anomalies much more efficiently.

Here are a couple of datasets for training your movement analysis models:

• The Moving INfants In RGB-D (MINI-RGBD) data set

• The KIMORE Dataset

Manufacturing

The manufacturing industry has already adopted a wide range of automation solutions with computer vision at its center.

It helps to automate quality control, minimize safety risks, and increase production efficiency.

Here are some of the most common computer vision applications in the manufacturing industry.

Defect inspection

Large-scale manufacturing sites often struggle to achieve 100% accuracy in defect detection in their manufactured goods.

Camera-based systems can collect real-time data and leverage computer vision and machine learning algorithms to analyze it and benchmark the results against a predefined set of quality standards. 

Check out how you can build your own defect detection models using V7.

It helps in identifying macro and micro level defects in the production line more efficiently.

This facilitates the error-free production process and decreases costs.

Reading text and barcodes

As most products have barcodes on their packaging, a computer vision technique called OCR can be successfully applied to automatically detect, verify, convert and translate barcodes into readable text. 

By applying OCR to photographed labels or packaging, the text they contain is extracted and verified against databases.

This procedure helps to identify wrongly labeled products, provide information about expiration dates, inform about product quantity in the magazine, and track packages at all stages of product development

Check out this guide to the best AI toots for OCR tasks here.

Product assembly

World-class companies like Tesla have already implemented automation of the product assembly line—the company reported the automation of over 70% of their manufacturing processes.

Computer vision generates 3D modeling designs, guides robots and human workers, identifies and tracks product components, and helps to maintain packaging standards.

Learn more about Artificial Intelligence in Manufacturing.