What Is Computer Vision and How Is It Disrupting Industries?
How Computer Vision Adds value to Businesses across Industries
Have you ever thought about how we humans observe the beauty of the world easily around us? For instance, when we walk at the park and admire the refinement of flower petals or enjoy children’s play and see their eyes sparkling with amusement. Scientists have spent decades to understand how visual system works to create computers that duplicate humans’ behaviour. Due to artificial intelligence advancements today, computer vision technology has been considerably integrated into our daily lives.
What is Computer Vision?
“Computer Vision powered by machine learning will change the way we see the world.”
- PwC
Computer Vision is a type of artificial intelligence that allows machines to interpret the visual world by replicating people’s perception of the world. Computer vision utilizes machine learning algorithms to make it possible for computers to recognize visual data, classify them and react to visual inputs. Different types of computer vision applications include motion capture, image segmentation, facial recognition, object tracking, object segmentation, classification, and optical character recognition.
Delivering Business Value with Computer Vision
The implementation of computer vision applications has seen a considerable increase over the past decade. Businesses from a wide variety of industries heavily invest in computer vision technology. According to statistics, the global computer vision market was estimated at USD 10.6 billion in 2019 and is expected to reach USD 19.1 billion by 2027. As data illustrates, the computer vision market is projected to see a compound annual growth rate (CAGR) of 7.6% from 2020 to 2027.
Computer vision technology has dramatically transformed the traditional way of conducting business. Companies can improve their customer experiences and increase business efficiency by implementing computer vision applications while automating business processes and reducing costs.
Computer vision applications
Computer vision facilitates companies to implement a wide range of innovations across various industries. According to the 2019 McKinsey & Company Global AI Survey, the adoption of computer vision technologies was widely covered by high-tech, automotive, telecom and healthcare industries.
Below are the most popular applications of the global computer vision market based on the Grand View Research segmentation:
· Quality Assurance & Inspection
· Positioning & Guidance
· Measurement
· Identification
· Predictive Maintenance
· 3D Visualization & Interactive 3D Modeling
Computer Vision in Healthcare
The healthcare industry has seen a wide range of advancements brought about by the rise of computer vision applications. Due to the technology, medical experts can diagnose diseases and health conditions while using X-rays, CT scans, and MRIs.
Disease Detection
According to Google, computer vision algorithms allow detecting cancer metastasis more accurately than human doctors. The effectiveness of computer vision while conducting medical scan analyses was also revealed in detecting diabetic retinopathy or diabetic eye disease (DED), which is a leading cause of blindness.
Blood loss measurement
Accurate blood loss measurement is another vital sphere where computer vision technology can be applied. Developed by Gauss Surgical, Triton, a blood monitoring solution has been successfully implemented in Orlando Health Winnie Palmer Hospital for Women & Babies to measure blood loss during childbirth. While expecting 14,000 babies to be born annually at the hospital, Triton has revealed that the real-time blood loss estimation decreased patients’ blood transfusions.
Medical Imaging Processing
Medical imaging applications facilitate physicians to interpret scanned images into 3D visualization models to define patient’s health conditions accurately. Furthermore, computer vision technology coupled with machine learning and virtual learning solutions significantly assist doctors in conducting surgical operations.
Computer Vision in Transportation
From autonomous vehicles, license plate recognition and traffic flow analysis to the possibility of pedestrian and parking occupancy detection, the implementation of computer vision technology has become the most apparent worldwide.
Autonomous vehicles
Self-driving cars gain more and more popularity across the world. By using computer vision algorithms, allowing to create 3D maps and motion estimation, automotive vehicles capture data on their surroundings, interpret it and respond accurately.
Tesla Autopilot is one of the notable examples of Computer Vision usage, facilitating the production of cars with complete self-driving autonomy. The vehicles are equipped with eight cameras offering 360-degree visibility at 250 meters of distance. Due to the ultrasonic sensors, Tesla cars can detect hard and soft objects, whereas a forward-facing radar allows vehicles to navigate during poor weather conditions.
License Plate Recognition
Automatic license plate recognition is a commonly used innovative application in vehicle registration, number plate recognition, vehicle logistics at ports, and others. OpenALPR is an automated number-plate recognition library that recognizes visual data on vehicle registration plates. While facilitating companies to use the full potential of the industries this technology serves, it provides considerable cost savings.
Parking Occupancy Detection
Computer Vision is already applied for visual parking lot occupancy detection using Convolutional Neural Network (CNN). CNRPark-EXT is one of the existing camera-based systems for parking lot detection that provides accurate data even during poor weather conditions.
Furthermore, coupled with license plate recognition, this technology provides data about cars occupying specific slots.
Computer Vision in Financial Services
Computer Vision is dynamically improving the financial industry. Besides digitalising financial services, the technology significantly helps financial institutions enhance customer experience more safely and accurately.
Know Your Customer Verification
Know Your Customer (KYC) is designed to provide financial institutions with detailed information about their clients. It facilitates organizations to utilize biometrics and identify their potential clients. Banco Bilbao Vizcaya Argentaria (BBVA) is one of many global banks which use computer vision for KYC verification. The banks’ clients can open accounts via a video call during which the customer verification process is being conducted. Such an approach significantly simplifies the verification process while reducing human interaction and the time needed for it.
Digital Payment & Enhanced security
The possibility of withdrawing money from ATMs without using credit cards is one of the significant advantages of computer vision technology adopted by banks. This process is possible due to the digital codes which customers receive on their mobile applications. In addition, biometric authentication usage like facial recognition and retina scanning allows financial institutions to improve security procedures and reduce fraud.
Conclusion
Being at the forefront of AI innovation, computer vision technology has considerably transformed the way companies operate today. It creates numerous opportunities for improvements across many spheres while enhancing customer experience, reducing business costs and wasted time.
Despite that computer vision can be of immense value in various industries, there are still promising ideas with unexplored potential. Handwriting recognition, sports tactics recognition and human gesture recognition are among such computer vision project ideas.
