How Computer Vision Applications Reshape Businesses and Solve Real-World Problems

Are you intimidated by artificial intelligence? Don’t be. This remarkable technology has already proven to be tremendously helpful to people and businesses. Take one of its subfields – computer vision. It allows machines, semi-controlled vehicles, drones, factories and farm equipment to work effectively and safely. You’ve probably experienced computer vision applications yourself without even knowing it. But how valuable can they be for your business? Let’s have a look.

Computer Vision Apps Across Industries: Use Cases and Opportunities

Unlocking your smartphone with your face, auto-tagging your friends on Facebook photos, stress-free parallel parking (yeah, it’s real) – all this is possible thanks to the computer vision technology. And it continues to immerse deeper into our lives. Here’s how different industries experiment with and benefit from computer vision.


As we know, AI is an expert in identifying patterns and analyzing complex situations. This makes the medical field a perfect playground for testing AI’s best skills. And since a significant part of patient treatment relies on various types of images, scans and visual checks, computer vision can complement and improve doctors’ work significantly.

  • Identifying strokes and brain injuries on CT-scans
    Many conditions are time-sensitive, so quick and precise diagnostics is life-critical. Unfortunately, doctors’ attention and decision-making can be affected by various factors: fatigue, urgency, time of the day, etc. But solutions like MaxQ AI aim at helping physicians and radiologists analyze patients’ brain scans to quickly identify strokes and brain injuries – conditions that require immediate treatment.
  • Assisting in radiotherapy treatment
    Computer vision apps can create a full 3D model of the tumor and the exact “border” between it and the surrounding healthy tissue. It gives doctors a precise vision of where the treatment should be applied and what organs can also be affected by the radiotherapy.Microsoft’s InnerEye is an excellent example of using AI in radiology. The FDA-approved computer vision application can detect anomalies on the uploaded images by highlighting the parts of organs or ligaments that contain tumors or other abnormalities.
  • Finding and pinpointing heart abnormalities, lung nodules, liver lesions
    Arterys, a US-based company with businesspeople and MDs in the founding team, built a medical imaging platform to assist physicians in scanning procedures and diagnostics. Viewer and Cardio AI were their first computer vision apps under the Arterys AI umbrella. They help to analyze scans and assist in finding anomalies on 3D computer images created from the uploaded scans. Similar AI applications are now trained to check lungs, liver and breast.
  • Analyzing and segmenting the medical scans of head and neck cancer
    With tumors located in the head or neck, it’s hard and time-consuming to draw a map of the areas to be treated and the ones to be avoided. DeepMind succeeds in applying computer vision for planning radiotherapy treatment in partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust. The app proves to be more efficient in the segmentation process than humans. The research is ongoing.
  • Monitoring blood loss during surgery
    The computer vision app Triton from Gauss can analyze the amount of blood on the lap sponges and suction canisters during surgical operations and labor. It has proven to be four times more efficient in hemorrhage recognition during C-sections than traditional visual analysis. The app recognized significant blood loss earlier than humans, which resulted in fewer blood transfusions and shorter hospital stays for the patients.Even an early stage of AI adoption shows that computer vision, deep learning and Big Data make a significant difference in diagnostics, treatment and patient care. More exciting and useful medical projects are sure to follow shortly.


Modern factories are complex and highly automated entities that require precise adjustment of their components and control over the entire system. Computer vision apps are widely used to make assembly lines more efficient by reducing defects, predicting breakdowns and ensuring high quality. Making it safer for people to work with heavy machinery is another advantage of vision technology. Here’s how manufacturers leverage it.

  • Predictive maintenance
    Since the production process consists of multiple components, ensuring the proper functioning of every detail is critical for safe and timely work. The combination of IoT and computer vision allows identifying potential failures through remote monitoring systems. For instance, robots from FANUC America have cameras that check for errors and send the visual data to the cloud. The app analyses these images and identifies potential problems. The parts are then inspected and fixed, with no downtimes in production.
  • Ensuring proper packaging
    For industries like pharmaceutics, the number, form, weight and color of pills or capsules is critical, as well as the integrity of the container or blister. Luckily, there are computer vision solutions that identify broken or incorrectly formed meds. The information about defects is recorded to the system, and the bottle with a faulty pill is rejected at the end of the production line.
  • Identifying defects
    The principle of defect detection is similar in all industries: the computer vision system detects abnormalities and deviances, plus, it can classify the defects by type and grade of deviance. This means that only a certain level or severity of defects will make the batch rejected or stop the production.
  • Reading barcodes
    Humans just cannot identify and process hundreds of thousands of barcodes, especially if they are as small as those on smartphones. Hand-scanning is exceptionally long and inefficient as compared to traceability solutions such as PanelScan. It can read barcodes while the products are moving along the line, with no errors and downtimes.
  • Distinguishing tiny details
    Landing AI developed a solution to find the defects on the level where the human eye cannot catch the problem. More than that, the app can do it 30 times faster than humans and process up to 400 objects per minute.

E-commerce and Retail

This industry is probably the most prominent adopter of computer vision. The competition drives innovations, and brands are trying their best to amuse customers and keep their attention. Here’re some creative ways of using vision technology in e-commerce.

  • Seeing offline, buying online
    Everyone has probably faced the challenge of liking something but not knowing where to buy it. Well, it won’t be a problem anymore. Just make a photo of the item you liked, and the new computer vision app Lens from Pinterest (beta version) will help you to find a similar thing online, offer design and style ideas or even food recipes.Visual Search from Cortexica has similar functionality: you can take a photo of a magazine page or drag an image from social media, and find a similar or the exact item to buy. You can also make a photo of your favorite pair of shorts and shop for the full look to match the style.
  • Recognizing loyal customers
    By using facial recognition and machine learning, businesses can identify their regular customers and treat them like VIPs. From greeting people by name to offering specific products because you know the customer’s preferences and habits, brands can win a high level of loyalty and create an ultimate marketing strategy.
  • Skipping checkouts
    You probably know the concept of the Amazon Go store in Seattle. In short, it is a store without shop assistants and cashiers. It registers customers via their smartphones and tracks the items people take from the shelves with cameras and sensors. All the items are recorded into a virtual shopping cart within the Go app. And then you just go away. The money is automatically charged from your card; the receipt is sent to the app. This is what we call efficiency.


With a direction towards autonomous and connected driving, computer vision is a critical technology for the car industry. We can experience some of its use cases even now, in our far from autonomous VWs and Citroens. But how can computer vision apps aid in reaching a higher level of autonomy?

  • Identifying potential threats
    Computer vision can give you the full visibility of the surroundings, not just the eyes on the bumper. This way, it can assist a driver in lane changing and merging or prevent the unintentional departure of the lane. The Insurance Institute of Highway Safety has estimated that more than 7,000 road fatalities could be avoided with lane departure warning systems. If the vehicle’s computer vision app saw that you swerve your lane or another car is crossing it, the app could alert you and, potentially, even automatically react to the threat. Also, vision technology can detect a pedestrian faster than a driver (due to weather conditions or distractions) and provide real-time alerts about the possible obstacles.
  • Fighting distracted driving
    A tired or drowsy driver is a top reason for road accidents. By recognizing the direction of the driver’s gaze and eye state (open or closed), computer vision apps can control the driver’s condition and issue an alert or stop the car when needed.
  • Aiding in traffic management
    Machine vision helps improve the traffic situation on the roads. From real-time feeds to the number of vehicles in the lanes, an AI program can dynamically control the flow, change traffic lights on intersections, inform road engineers about problems and more. On top of that, with the future connected driving, computer vision apps will communicate with the vehicles directly and inform them about the obstacles ahead.


Even a conservative field like finance is adopting computer vision. That’s because it pays off. Machine vision aids in making investment decisions and validating property for creating proper insurance plans. Here’s how.

  • Property analysis
    With the help of computer vision, software can analyze satellite images of the client’s property, from the state of the building’s roof to the surroundings that may influence the insurance plan. No physical inspections, only accurate quotes and timely plan updates – all this is possible with the Cape Analytics AI application.
  • Investment strategies
    Can geospatial images from drones, airplanes, and satellites help in predicting social and economic trends? Surprisingly, yes. Information analysis is a background of successful investment, and Orbital Insight provides hedge funds and investment companies with valuable data. This includes the state of the construction of the new city section, the number of cars on the Walmart parking lots all over the country or even how the speed of iron ore mining has changed over the last year. Impressive, isn’t it?

Final Thoughts

Artificial intelligence is still far from outperforming the human brain. But different sub-fields of AI are making our lives better right now. Intelligent chatbots, a stellar level of data analysis, personalized customer experience, driver assistance systems – these perks of our modern era are praise to AI.

Computer vision apps are a part of the improvement in our everyday lives: from smart home systems to safe driving, to better hospital diagnostics. The technology has proven that it can bring profits and the ROI is high. That’s why businesses in various industries are implementing computer vision in their operations. Now is the time to ask yourself: how can my business benefit from computer vision? If you’ve got ideas, don’t be shy to share them with Skelia.


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About Skelia

Skelia, a Nortal company, is an international leader in building cross-border IT and engineering organizations and affiliate companies in Eastern-Europe. For over a decade, we have provided staff augmentation services to a diverse range of clients—from start-ups to Fortune 500 companies. We operate in Luxembourg, the UK, the Netherlands, Ukraine, Poland, and the US.