Computer vision is no longer a technology of the future: it is a reality we now find everywhere, often without even realizing it. From smart surveillance systems to self-driving cars to applications that read X-rays or analyze agricultural crops, the ability of machines to “see” and interpret images is profoundly transforming many aspects of our daily lives. We addressed these aspects in article Computer vision and artificial intelligence.
In this article, however, we will take you on a tour through five key areas where computer vision is showing its full potential: transportation, healthcare, manufacturing, retail, and agriculture. For each of these areas, we will explore concrete use cases, show how applications based on artificial intelligence are becoming increasingly refined and widespread, and tell how they are improving efficiency, safety, and quality of services. A journey between innovation and reality, to understand how the “digital eyes” of machines are changing the world around us.
Computer vision in mobility and transportation
Self-driving cars would not be possible without computer vision. But buyers of autonomous vehicles (AVs) are not the only ones touched by artificial intelligence. If you have ever used a car with a backup camera that warns you of the presence of nearby objects, you have encountered computer vision technology. Indeed, in recent years, computer vision applications have reshaped everyone’s experience on the road.
Here are just a few examples of computer vision applications in the transportation sector.
Autonomous vehicles
Autonomous vehicles rely on four key elements to process images and make real-time driving decisions: the car’s sensors, connectivity, a high-precision positioning system, and machine learning algorithms.
Autonomous vehicles use these tools to apply a variety of computer vision techniques in real time:
- Pattern recognition, for example, to classify objects such as traffic signals and traffic lights.
- Object tracking – to respond to moving objects such as other cars or pedestrians
- Image segmentation – to identify and select relevant features from the raw data, such as the focus of pedestrians despite a crowded background
- 3D vision – to understand the relative position of objects and navigate in space.
These tasks are derived from a series of machine learning algorithms trained for perception and decision making.
Intelligent toll systems
Modern toll systems do not require motorists to stop and pay a fee with exact change. In fact, they use computer vision to dynamically collect payments, identify violators, and analyze traffic flow.
Smart toll systems can classify vehicles by vehicle type to collect the correct toll payment. License plate recognition systems use optical character recognition (OCR) to read license plates from images or vehicle video surveillance. The system can verify these numbers against a database of vehicle registrations. The system compares the license plate number with the driver’s toll bill or looks for where to send the bill.
Traffic flow analysis and traffic management
Algorithms can identify and follow pedestrians in a scene. It is important to recognize pedestrians, regardless of what they are wearing or how they move. Similarly, traffic cameras count vehicles and monitor traffic flow. Computer vision can analyze traffic density on highways and behavior at urban intersections. All of these analyses inform traffic management to reduce traffic jams and improve road safety.
Computer vision in healthcare
It is difficult to overestimate the extraordinary power of computer vision in healthcare innovation. Computer vision has advanced the diagnosis, care, and treatment of patients in real time. We have already mentioned the advantages of computer vision in diagnostic imaging. Computer vision systems can detect subtle abnormalities that indicate diseases such as cancer, pneumonia, or Alzheimer’s.
Because artificial intelligence can detect certain patterns and features better than a human could, the medical application of computer vision makes earlier interventions possible and improves patient outcomes. In addition, computer vision in medical imaging has been shown to help healthcare providers reduce their workload by 88 percent.
Computer vision can also be a second pair of hands. Voice recognition technology is ineffective in noisy and chaotic environments, such as an operating room or emergency room. In addition, the use of a keyboard or touch screen requires on and off. With hand tracking and gesture recognition, physicians can read images using gestures to save valuable time.
Diagnostic imaging
Missed tumors and false positives can be devastating for patients, so we are always trying to improve accuracy and specificity. The application of computer vision technology is helping us.
Modern artificial intelligence algorithms can detect cancer as well as specialized radiologists. Moreover, because computers use different methods than humans, they can detect features that human eyes cannot. But it turns out that humans and machines work better together. The researchers found that radiologists are better at detecting cancer with the assistance of artificial intelligence than without it. Moreover, working with computer vision did not require additional time.
Computer vision has transformed medical imaging, including breast cancer screenings, lung scans, brain scans, and cardiovascular imaging.
Dermatology
If radiology has been quicker to adopt computer vision, this technology is just as powerful for dermatology. According to the Skin Cancer Foundation, skin cancer is the most common cancer in the world. As with other cancers, early detection is critical. Computer vision projects are improving skin cancer diagnosis and reducing the workload of operators. Computer vision applications can also put screening tools in the hands of patients in the form of smartphone apps.
Machine vision in the manufacturing sector
As mentioned above, machine vision and computer vision methods are both used in manufacturing. With the increasing prevalence of robotic automation in manufacturing processes, computer vision is becoming more sophisticated. Visual intelligence plays a key role in perfecting machine vision applications in industry.
Predictive and preventive maintenance
Equipment maintenance is critical for worker safety and to minimize downtime. Computer vision can be used to monitor production equipment for signs of wear and tear. A computer vision system can constantly scan for changes to prevent breakdowns. This is known as predictive maintenance. Artificial intelligence can also identify minor problems and flag them for repair before they cause problems. This is known as preventive maintenance.
Quality control
Quality control is a crucial step in production, but manual inspection is labor-intensive. In the past, manufacturers used lightweight machine vision systems to automate this process. Now that artificial intelligence is more accessible, factories want more robust machine vision programs.
Machine vision systems were demanding and required specialized cameras and very particular image parameters. Because new artificial intelligence systems use machine learning, they are more flexible with regard to inputs. Computer vision applications can identify parts and defects in almost any context. This means that a computer vision program can operate in multiple establishments.
If you are concerned that this flexibility means a reduction in accuracy, don’t worry. One study analyzed a computer vision algorithm charged with checking the quality of brake parts. Defects in these parts are too small to be identified by humans, but the algorithm achieved more than 95 percent accuracy in detecting them.
Computer vision in the finance and insurance industry
Security and fraud protection are critical in finance and banking, and computer vision offers many options. It makes transactions more secure, simplifies the processing of insurance claims, and enriches financial planning.
Cash withdrawal with facial recognition
Traditional ATM transactions rely on PINs and physical cards, which can be lost, stolen, or compromised. With computer vision, ATMs can now authenticate users through facial recognition, allowing customers to withdraw cash simply by looking at the machine’s camera. This technology enhances security by ensuring that only the account holder can access their funds, reducing the risks of card skimming, PIN theft and unauthorized access. Biometric verification also speeds up the withdrawal process, making transactions smoother and reducing wait times at ATMs.
Augmented reality for financial planning
Financial planning can be complex, but augmented reality (AR) powered by computer vision makes it more interactive and accessible. AR applications allow users to visualize financial data in real time, showing information such as budget trends, investment performance and retirement savings projections in a dynamic 3D format. By integrating AR into banking and wealth management applications, institutions can offer customers a more personalized and engaging financial planning experience.
Damage assessment through image analysis
Insurance companies have traditionally relied on manual inspections and paper-based processes to assess damage from accidents or disasters. Computer vision now allows insurers to analyze images of damaged vehicles, property or assets to determine the extent of damage and quickly estimate repair costs. This artificial intelligence-based approach speeds up claims processing, reduces the need for in-person inspections, and minimizes human error. By automating damage assessment, insurers can provide faster claims to policyholders and more efficiently detect fraudulent claims.
Computer vision in retail
Because the retail sector has many moving parts, computer vision also has many applications in the retail sector. These include both customer-facing applications and behind-the-scenes activities.
Customer data management
Facial recognition and object tracking help companies understand customer behavior. Don’t worry, we’re not talking about a dystopian invasion of privacy. On the contrary, computer vision opens the door to aggregate data about customers that can improve the shopping experience for everyone.
A computer vision system can help with retail analytics. It can count people, measure wait times and identify customer behavior patterns. It can track customer routes through a space and provide recommendations, much like traffic flow systems in the transportation industry. For example, heat maps can indicate ideal locations for key products. Computer vision can also provide guidance for optimizing a store layout to increase sales and reduce losses.
Self check-out
Going through self-check-out can be frustrating for customers buying produce or other bulk items. They usually have to check out the item code and type it in themselves. POS devices equipped with cameras and artificial intelligence can recognize bulk items. Customers enjoy a more convenient shopping experience and lines move more quickly. Walmart even uses software that detects missed scans.
Stores without checkouts
Cashless stores, such as Amazon Go, take smart self-checkout a step further. Computer vision and deep learning techniques track each customer and detect the items they take while shopping. When customers leave the store, the system bills for the items, without the need for a manual check-out process.
Artificial vision in farming
Agriculture is getting a technological boost from computer vision applications in agriculture. Smart agriculture technology uses visual data and machine learning to improve agricultural practices.
Smart greenhouses and farm management
Indoor food production is increasing in places that do not support traditional agriculture. Vertical farming and greenhouses make it possible to grow in arid environments and urban settings. But to keep plants healthy, these climate-controlled facilities must be monitored 24 hours a day. Unified farm management solutions automate monitoring and control. With these systems, smart greenhouses and vertical farms practically take care of themselves.
Real-time land monitoring and management
Agricultural drones with cameras and mapping solutions have been a game changer for farmers. Use cases include monitoring crop growth and health and monitoring livestock. Farmers can also observe pests and monitor soil moisture. With real-time data, farmers can apply water and interventions exactly when and where they are needed. These computer vision applications save money and water and reduce the use of chemicals. This helps the farm, improves soil health and maximizes yields.
The challenges of adopting computer vision
Although the development of cloud computing and open technologies has made computer vision more accessible, this does not mean that it is easy to get started on your own. The technology is complex and requires a lot of investment and resources. Although it offers tangible benefits, implementing computer vision solutions can exacerbate critical technological challenges such as visual data integrity and diversity, dimensional complexity, variability in data labeling and categorization, as well as ethical considerations and inter-organizational availability.
Many organizations encounter problems with computer vision and its solutions before they can implement robust and efficient systems:
- Complexity and scalability – Machine learning operations (MLOps) require AI and ML skills, which most organizations do not have in-house.
- Cybersecurity – When dealing with huge volumes of data, it is critical to apply best practices in privacy, security and compliance.
- IoT expertise – Computer vision programs require IoT (Internet of Things) solutions and services that are best entrusted to an experienced IoT service provider.
Computer vision is here to stay
Computer scientists have spent decades putting computers in a position to perceive the world around them, enabling humans to use machines to meet their needs. Today, computer vision applications are reshaping the environment around us, but the technology is only scratching the surface of its potential.
In the near future, we expect computer vision algorithms to become increasingly robust and pervasive, leading to potentially disruptive new applications.
As GenAI technology is making many changes, computer vision is also expected to undergo changes. For example, the ability to generate synthetic data can improve the training of computer vision systems, such as those used for facial recognition and object detection, making it more cost-effective and less invasive to privacy. It can also speed up the labeling of training data, a traditionally laborious and expensive task when performed manually by humans.
Technology for extracting real-time information from live video has matured and is expected to expand further. Already used in crowd scanning, security surveillance and factory monitoring, real-time computer vision is poised for valuable new applications as algorithms advance.
By applying computer vision to satellite imagery, we can monitor various activities on Earth, including deforestation, the spread of floods and fires, urban sprawl, and the dynamics of marine ecosystems. As satellite imagery and computer vision algorithms advance, we can expect insights that will facilitate more timely interventions and optimization of resource use.
In addition, computer vision is expected to understand and reduce the risks associated with technological development. Many believe that computer vision is critical to addressing the threat posed by increasingly compelling deepfakes generated by artificial intelligence. Its ability to examine images and detect clear signs of algorithmic creation is critical for distinguishing real content from computer-generated content, making it important for addressing concerns about propaganda and the detection of disinformation. Issues of bias and correctness permeate all aspects of AI, but are particularly relevant in computer vision. For example, facial recognition algorithms often demonstrate less effectiveness in identifying individuals with darker complexions, increasing the potential for error, especially in surveillance or law enforcement contexts. In the coming years, more emphasis is likely to be placed on privacy-focused artificial intelligence and computer vision technologies, such as automatic face blurring, designed to operate in public spaces without violating privacy rights.
