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Health Care

Use Case

AI has the potential to change medicine radically and can help provide affordable healthcare to millions of deserving people.

Medical diagnosis relies on CAT scans, MRI images, X-rays, sonograms, and other images. Computer vision applications help diagnose patients by processing, analyzing, and categorizing images and can process thousands of images in minutes or even seconds. Computer vision can automate tasks such as:

  • Detecting physical irregularities in the body by processing thousands of images quickly.
  • Mitigating the consequences of human error in reading medical images. Save time in critical cases where earlier treatment means the difference between life and death.
  • Sorting and extracting medical images at a remarkable pace to assign diagnoses.
  • Computer vision applications have enabled phenomenal and continuing superiority in modern medicine. The applications will only outperform themselves as time and technology evolve.

Smart Cities

Use Case

There are several applications of computer vision in smart cities:

  • Implementation of computer vision for smart cities relies on cameras and sensors embedded in roadways to monitor pedestrians, traffic density, and traffic flow. A great example is a trail monitoring system, such as the one built by productOps in conjunction with the alwaysAI platform.
  • Gauging general park engagement and determining maintenance schedules.
  • Enforcing proper trail usage.

productOps was tasked with building a live video monitoring system that could identify how the trails were being used as well as identify rule-breakers causing degradation to the trails. They decided to use alwaysAI for its ease of use and because it allowed real-time object identification and lightweight data transfer.


Use Case

Equipping robots with machine learning computer vision increases their capabilities and abilities to perform more complex tasks.

There are several applications of robotics across various industries.

alwaysAI engineers built an application on a demo robot showcasing the powerful combination of the Qualcomm RB3 platform with alwaysAI. This robot demonstrates real-time object detection and pose estimation and can detect an elderly person falling and triggers a response to call for help.

The alwaysAI platform has also been used at the First Robotics Competition, by competitor Christian Piper and his FRC team, another example of the alwaysAI platform equipping a robot with machine learning computer vision. alwaysAI platform makes it easier and more accessible for competitors to equip their robots with intelligent sight.

Mobile robots are used in manufacturing with automated guided vehicles to help reduce human labor by transporting materials around a production facility. In fact, according to Interact Analysis, by 2023 the market for autonomous mobile robots (AMRs) and autonomous ground vehicles (AGVs) is expected to generate over $10 billion. The immense growth this industry has experienced, combined with rising labor costs has fueled this demand for robotics in order to provide customers with one day or same-day delivery.

Marine Industry

Use Case

Artificial intelligence, robotics, and machine learning are transforming the marine industry. Seafloor Systems Inc. is taking the lead in this domain, using the alwaysAI platform.

Seafloor Systems Inc., located in Northern California, is known for its innovative application of hydroacoustic sonar and unmanned marine surface vehicle technology. By adding computer vision capabilities to their unmanned surface vehicles, their future fleet of autonomous marine surface drones will avoid collisions with marine life and boats as they map and monitor the seafloor.


Use Case

There are several uses of computer vision in manufacturing. Inspections automated with machine learning computer vision models will result in more efficient and accurate, yet lower-cost inspections.

One specific example worth highlighting is aircraft inspection.

A single mechanic may take up to 10 hours to inspect the whole aircraft for damages and possible areas of failure. With computer vision cameras, an aircraft part is inspected in seconds at a much lower cost. Productivity increases, thus enabling companies to redirect resources, such as mechanic and engineering hours, to other projects for growth and expansion.


Use Case

Computer vision will be an essential component of agriculture automation, helping farmers minimize their cost of production and improve the crop yield with better efficiency.

AI robotics can help perform tasks such as:

  • Planting
  • Weeding
  • Harvesting
  • Plant health detection

Flying drones can capture a considerable amount of data through their cameras equipped with intelligent sight, which can be used to monitor the health of crops and check soil conditions through geo sensing and visual sensing.

Semantic segmentation, object detection and object counting can be used in agriculture, from product to livestock.

Waste Management

Use Case

Approximately 1.3 billion tons of garbage are produced globally on a daily basis.

Garbage sorting and disposal can be automated by using AI and computer vision for smart recycling and waste management. Trash bots can be used to sort through waste and identify bottles, cans and other recyclable items. Object detection can be used to automate the sorting of items in landfills.


Use Case

Computer vision can be used to keep public spaces densely populated areas safe.

It is affordable to equip security cameras with computer vision, and makes them much more accurate in detecting security threats. In large public spaces where there is a lot of traffic, security cameras can generate a high amount of video streams to be processed. No human can digest dozens of video streams continuously whereas computer vision can be more accurate.

Computer vision cameras can continuously monitor people and patterns and look for indications of security concern as well as systematically search a field of view for objects of interest.

Smart Home

Use Case

Computer vision will take smart homes to the next level, as intelligent sight offers advanced sensing solutions for home devices. When systems can detect and recognize objects, they can deliver smart actions according to what they were programmed to do.

One example is a refrigerator using computer vision to look into a fridge and assess what is needed and trigger an order from amazon fresh or other home delivery services. For the elderly and disabled, robots can even call for help if they see that someone has fallen, or assist with mobility around the home.

The alwaysAI platform is now integrated with Twilio so you can interact with the smart cameras in your home. You can have automated text messages sent to you and be notified in real-time when an event occurs at your home.

Marketing & Retail

Use Case

Retail and restaurant establishments can benefit from computer vision to acquire data on customer behavior and demographics.

In addition to providing retailers with demographic insights, computer vision can help brick and mortar retailers by assisting with planograms. A planogram is a diagram of retail products showing how they should be placed on the shelves to increase customer purchases. Usually, companies hire a professional to do this and have them regularly check the merchandise arrangement.

The average cost for manually generating a planogram report is $72, but with the help of AI and computer vision, this can be reduced to $8 per report, which is a 9x savings. Retail owners are expected to spend around $34 billion on AI technology by 2025. 29% of this amount is predicted to go to computer vision for its ability to facilitate inventory reports, contribute to customer analytics, and empower checkout-free shopping models

Autonomous Vehicles

Use Case

Computer Vision is an essential component of autonomous vehicles.

Detecting pedestrians and bicyclists in a cityscape scene is a crucial part of autonomous driving applications. Autonomous vehicles need to determine how far away pedestrians and bicyclists are, as well as what their intentions are.

Semantic Segmentation is the best tool to use for detecting pixel-by-pixel rather than with bounding boxes. Accurate object avoidance in real-time is essential for passenger and pedestrian safety.


Use Case

There are many applications of Computer Vision in education.

Computer vision can maximize students’ academic output by providing a customized learning experience based on their individual strengths and weaknesses. This is accomplished by observing students’ behavioral patterns during the learning process to gauge engagement.

This is especially relevant and beneficial in online learning situations. With computer vision, online educators can analyze user behavior, eye movement, and posture to assess engagement levels. This helps educators study student behavior in order to help guide future courses.

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