Computer vision, the field of AI, is expected to hold the biggest market share and is estimated to reach $41.11 billion by the end of 2030. As it continues to gain importance in digitally transforming the businesses, leaders worldwide are constantly exploring ways to harness its potential in their respective industries.
For them, here is the real-life computer vision use cases that are already transforming healthcare, retail, automotive, transportation, manufacturing, and several other industries.
Computer Vision in a Nutshell
It is a part of the artificial intelligence field that replicates human vision to let computers see and comprehend their surroundings exactly as we do. Deep learning, machine learning, parallel computing, and other technologies are blended together to make this happen.
And the availability of quintillions of data that we generate every day helps in training computers to improve their knowledge and understanding of a variety of objects. Using this knowledge, these machines are not only able to classify or differentiate objects, their color, and size but can also do intelligent tasks like humans.
Computer Vision Use Cases in Manufacturing, Healthcare, Retail, and Beyond
This technology has tremendously revolutionized the manufacturing industry by providing an automated system to identify minute defects in products. Using machine learning algorithms, computer vision systems can detect subtle variations in product quality that may not be visible to the human eye. They are trained to recognize different types of defects, such as scratches, dents, cracks, and other imperfections. They can also detect variations in color, shape, and size, ensuring that each product meets the specified quality standards.
As a result, manufacturers can significantly reduce the number of defective products that make it to market, improve product quality, reduce waste, and save costs associated with scrap and rework.
Other than manufacturing industry, computer vision is proving a valuable technology in these industries as well. Check the use cases here:
1. Advanced Robotic Surgery in Healthcare
Robots performing surgery is already an advancement in healthcare but computer vision multifold their ability to perform even delicate and complex procedures that would otherwise be difficult for a human to perform. System vision is enhanced by real-time, high-resolution images of the surgical area, allowing the surgeon to have a better view, and understanding of the procedure.
Computer vision algorithms can even track the movement of the instruments, ensuring that they are precisely positioned and controlled. One of the benefits of computer vision in healthcare is improved patient safety as robots can provide a minimally invasive approach to surgery thus, reducing the risk of complications. Additionally, computer vision algorithms can track vital signs and monitor for any adverse events during the procedure, allowing for early intervention if necessary.
Other uses of computer vision in healthcare include:
1. Patient monitoring in a home-based rehabilitation environment
2. Early detection of disease and infection prevention
3. Help visually impaired or disabled patients
4. Medication management
5. Automating cell counting and detecting infected cells
6. Ensuring hospital hygiene compliance
2. Virtual Mirrors in Retail Industry
Computer vision is a game changer in giving an unforgettable personalized shopping experience to customers, which is the topmost priority of the retail industry. The technology is used along with AR and ML in virtual/smart mirror software to transform the way customers shop. The trio work in the following ways:
• Computer vision cameras scan customers’ bodies and provide accurate measurements to identify the outfits of right size for them.
• AR overlays the customer’s image with digital clothes so that they can try different outfits without even trying them for real.
• ML provide the best fashion recommendations to the users based on their current outfits or style, body type, and skin tone.
3. Camera-based Customer Analysis Applications
Analyzing customer behavior and preferences using camera-based applications is one-way computer vision is making difference in the retail industry. These applications provide valuable insights into customer behavior, helping businesses to improve customer engagement and enhance the overall customer experience. Some of the analyses performed with the help of these applications are:
• Foot traffic analysis to find the most popular areas, peak times, and shopping patterns.
• Customer engagement analysis to check engagement with products, displays, and advertisements in retail stores.
• Queue management to manage wait times, reduce customer frustration, and improve the overall customer experience.
• Employee performance analysis to identify areas for improvement and to provide training and coaching wherever needed.
• In-Store analytics to gather data such as the number of customers entering and leaving the store, the amount of time customers spend in the store, and the products they interact with.
However, it is important to ensure that the use of cameras for customer analysis is done in a privacy-sensitive manner, with clear policies and procedures in place to protect customer data and privacy.
4. Cashier-less Stores
One of the growing applications of computer vision in retail is self-checkout stores or cashier-less stores that reduce the wait time of customers on billing counters. Amazon Go stores in USA are the real-life example that use computer vision technology. These stores do not have any cashier and customers simply purchase items of their choice and leave without having to check out. The total price of the purchased items is directly added to customers’ wallet or account.
5. Driverless Trucks in Transportation & Logistics Industry
The driver shortage is the biggest problem in the industry, especially in America. According to statistics, there is a shortage of around 80,000 drivers, and the number is expected to double by 2030. As a result, the stores are facing product scarcity and companies are struggling to meet 100% turnover.
Self-driving or autonomous trucks solve this issue, making transportation efficient and resilient. Computer vision is a key technology in the development of these driverless trucks. For giving the vision, the trucks are equipped with hardware such as cameras, sensors, and GPS systems.
On the software front, deep learning and computer algorithms are used to gather information about the surrounding environment through hardware to make smart decisions based on that information.
Computer vision empowers autonomous trucks to:
• Navigate the roads safely and efficiently
• Detect obstacles, such as other vehicles, pedestrians, or road signs, and respond appropriately
• Identify lane markings and road boundaries, and use this information to control the vehicle's speed and position on the road
• Optimize its route by finding the shortest path to its destination to avoiding traffic congestion
• Detect potential safety hazards, such as other vehicles that are driving erratically, and take appropriate action to avoid a collision
The use of computer vision in autonomous trucks can significantly reduce the need for human drivers, thus addressing the driver shortage issue. This also help in increasing productivity and reduce costs for transport and logistic companies, making autonomous trucks an attractive solution for them.
6. Monitoring Product Assembly Sequence
Computer vision technology is changing the way manufacturing industries are ensuring the right sequence of product assembly. With the help of this technology, operators can now track the assembly sequences in real time and also get immediate visual feedback in case of any error.
One example of this use case is, checking the proper torquing of bolts while assembling the engine. Other important sequences of the assembly process such as proper sealing of the gaskets, and proper alignment of components like bearings, etc. can also be accurately monitored with the use of computer vision. These processes are very crucial in the product assembly process and any deviation can cause catastrophic failure.
7. Industrial Infrastructure Inspection
The traditional method of inspecting industrial infrastructure that relies on human eyes is slow and laborious. Often, it involves risk when the inspection site is in a remote area or involves dangerous activities like climbing cell towers. Furthermore, the traditional method is full of errors due to the shortage of appropriate training and experienced inspectors. Providing them with adequate training and insurance makes the process expensive as well. Around $1,000/mile is spent on aerial inspections by utility companies.
Computer vision mitigates all these challenges at once and also makes the inspection and monitoring process cost-effective. Along with high-definition IoT-connected cameras, using high-resolution satellite images, edge servers, and drones, computer vision allows instant detection of anomalies in the industrial components.
It also enables real-time decision-making and predictive maintenance, thereby reducing downtime and increasing efficiency.
8. KYC Verification in Banking & Finance
Know Your Customer (KYC) is an important process in the banking and finance industry that helps ensure that financial institutions are complying with anti-money laundering (AML) and anti-terrorism financing (ATF) regulations.
Computer vision enhances this process and makes it more secure and fast, unlike the traditional method that requires manual verification of customer identity and supporting documents. How?
• Automating the process of scanning and verifying important identification documents, such as passports and ID cards, ensures that they are authentic and belong to the person claiming to be the owner.
• Facial Recognition to verify the identity of customers ensuring that the person opening an account or accessing financial services is who they claim to be.
• Integrating with other identity verification technologies, such as biometrics and machine learning, to create a comprehensive and secure verification process.
• Analyzing images, videos, and other data sources to detect fraudulent activity and prevent money laundering and other financial crimes.
• Streamlining the customer onboarding process by automating the verification of customer information and documents, reducing the time and resources required to complete the KYC process.
Other computer vision use cases in the banking and finance industry are:
• Fraud Detection
• Risk Management
• Customer Service
• Investment Management
• Loan Application Processing
As the technology continues to grow, computer vision use cases will also increase thus reducing the long-persistent challenges faced by several industries. The technology shows promising results in dealing with those complex challenges with ease and brings real-time solutions that are also cost-effective for the industries. The applications mentioned in the blog are just the tip of the iceberg. Substantial efforts are put in by tech experts to utilize computer vision at full speed for improving process efficiency and accuracy. So, it is not wrong to conclude that computer vision is the future.