Computer vision uses computers to simulate the human eye in identifying, tracking, and measuring targets, while also recognizing, interpreting, and processing graphics and images. In other words, it enables computers to “understand what they see.” It has become one of the key technologies in artificial intelligence to achieve major breakthroughs early on, with relatively clear application scenarios.
With the continuous progress of deep learning, the expansion of computing and storage capacity, and the explosive growth of visual data, computer vision has developed rapidly in recent years. In fact, in less than a decade, the accuracy of computer vision technology has improved from 50% to 99%, while its business applications and market size have continued to grow. In 2020, the global computer vision market was valued at USD 9.45 billion, and it is expected to reach USD 41.11 billion by 2030. A recent report by Forbes listed five industries where this technology is most likely to show its full potential.
Healthcare
In recent years, the healthcare industry has increasingly adopted computer vision technology to improve patient outcomes and increase efficiency.
One major application of computer vision in healthcare is the analysis of medical scans. It can detect abnormalities in an individual patient and also identify patterns across thousands of scans, giving doctors valuable information about specific diseases. Computer vision can often notice patterns that the human eye cannot detect. For example, subtle differences in the appearance of certain cancer cells may only be identified through computer vision and AI analysis.
A study on breast cancer screening showed that AI-based vision systems were more accurate than human radiologists in identifying signs of breast cancer in mammograms. This reduced both false positives and false negatives, while cutting human workload by 88%.
For example, last year the UK and the EU approved a breast cancer diagnostic technology called PANProfiler for clinical use in healthcare services. It can provide an initial diagnostic reading of an image in just 15 minutes, with accuracy comparable to laboratory testing methods that may take weeks. This offers a faster and lower-cost alternative to traditional testing.
Computer vision is also being used to prevent accidents in hospitals. For instance, AI-powered cameras can detect if a doctor forgets to sterilize a tool during surgery or accidentally leaves a foreign object inside a patient, and then alert staff that something has gone wrong.
Retail
Computer vision is also making a strong impact in retail. Retailers can create heat maps and analyze customer movement paths to better understand in-store behavior, allowing them to test different marketing strategies and increase sales.
For example, Amazon is using advanced computer vision technology to let shoppers leave the store without scanning items or stopping to pay. AI detects which items the customer has taken, and the system charges the shopper’s Amazon account automatically.
Computer vision can also significantly improve inventory management. The technology can identify items and count crates in images or videos without requiring workers to do manual stock counts. These automated inventory checks provide real-time updates, helping retail staff make better decisions about stock levels. It is reported that 64% of retailers plan to deploy data-driven solutions such as computer vision in the coming years to optimize inventory management.
Automotive
Computer vision also has wide applications in the automotive industry. In manufacturing, it can detect product defects and help ensure that products meet quality standards. Cameras placed on the production line can identify defects and alert workers in real time. In one study, a computer vision algorithm was able to detect faults in brake components with an accuracy rate of 95.6%.
In addition, computer vision is a critical part of today’s autonomous vehicles. The technology can identify objects on the road, create 3D maps, detect lane markings, and help drivers operate in low-light conditions. In 2021, electric vehicle maker Tesla announced that its new cars would rely entirely on computer vision rather than LiDAR. The company’s chief AI scientist said that deep learning systems are “a hundred times better than radar.”
Food Service
The food service industry was one of the sectors hardest hit by the COVID-19 pandemic. Many businesses were forced to digitize and innovate in order to survive. More and more restaurant chains are adopting AI-driven innovations to improve efficiency and reduce costs.
Computer vision technology helps restaurants reduce long customer wait times, optimize the use of floor space, and even monitor whether customers are following mask rules.
For example, one startup is using computer vision to help fast-food restaurants reduce incorrect orders and improve operational efficiency. Another startup is using computer vision to help restaurants speed up processes and evaluate customer experience. Businesses are using this technology to measure how long customers wait in restaurants and to upgrade their security systems.
Energy and Utilities
In the energy and utilities sector, computer vision is improving operational efficiency, enhancing safety, and helping prevent accidents.
For example, workers can use computer vision to analyze images of utility poles and detect defects that may lead to fires. Utility companies can then determine whether these abnormalities require immediate attention and take action to prevent extreme incidents.
Beyond anomaly detection, computer vision is also being used to improve workplace safety in energy and utility operations. For example, deep learning algorithms can analyze video in real time, detect violations of safety protocols or intrusions into work areas, and alert employees to potential dangers.
Outlook
As Forbes noted in its report, computer vision can improve efficiency, save time and resources, increase accuracy, and strengthen safety. For these reasons, it is expected to see even wider adoption in the coming years.