How Five Industries Are Using Computer Vision Technologies
Computer vision has been around for decades, but in recent years, this technology has been advancing by leaps and bounds. Computer vision is an application of artificial intelligence (AI) that's able to understand and interpret images, identifying and reacting with near-perfect precision due to developments in neural network technology. In fact, the accuracy of the technology has gone from 50% to 99% in less than a decade.
As computer vision becomes more advanced, its business applications grow. The global market size of computer vision was valued at $9.45 billion in 2020 and is projected to reach $41.11 billion by 2030. Computer vision has applications across a wide range of industries, but in this article, we’re going to zoom in on five of the most promising sectors.
1. Energy And Utility
In the energy and utility industry, computer vision is powering more efficient operations, improving safety and helping prevent harmful accidents. For example, computer vision-powered analyses of images of electric poles can detect defects in the poles that may spark and turn into fires. Thanks to predictive maintenance technologies that flag these anomalies, utility companies can then make a decision as to whether the defect needs immediate attention—and prevent events as extreme as wildfires.
In addition to defect detection, computer vision applications in the energy and utility industry also include safety in the workplace and onsite. Deep learning algorithms can spot violations of safety protocols or intrusions of work zones by analyzing videos in real time and alerting staff members of the danger.
The restaurant industry was one of the hardest hit by the pandemic, and many establishments were forced to digitize and innovate to stay afloat. Increasingly, restaurant chains are adopting AI innovations to help them drive efficiencies and minimize costs.
Computer vision technology has allowed restaurants to reduce long customer wait times, optimize the use of their floor space and even monitor mask compliance. For example, one startup is using computer vision technology to help quick-serve restaurants minimize incorrect orders and improve operations. Meanwhile, another startup is leveraging computer vision to help restaurants speed up processes and evaluate the customer experience. Businesses use the technology to measure the amount of time spent in the drive-thru and waiting in the dining room, as well as to upgrade their security systems.
In recent years, the healthcare industry has been increasingly leveraging computer vision to improve patient outcomes and drive operational efficiencies. One of computer vision’s main applications in healthcare is to analyze images of scans, both to detect abnormalities in an individual and to identify patterns across thousands of scans that may inform physicians’ knowledge about a certain condition. Computer vision is often able to notice patterns that the human eye can't pick up.
In fact, the results of a study on breast cancer screening show that visual AI systems demonstrated more accuracy than human radiologists when looking for signs of breast cancer in mammograms, reducing the number of both false positives and false negatives. By augmenting their analysis with computer vision, the human providers were able to reduce their workload by an astonishing 88%.
And it’s not just in scan analyses that computer vision can support healthcare outcomes. The technology is also being used to prevent accidents in the hospital. For example, a camera powered by computer vision can detect when a provider has forgotten to sterilize a tool or left a foreign object in a patient during surgery and subsequently notify them of the mistake.
In the retail sector, the applications for computer vision are truly exploding. For example, retailers can create heatmaps and analyze footfall, which provides insights into customer behavior in the store. This allows them to experiment with different merchandising strategies to increase sales.
Amazon is one well-known retailer that uses advanced computer vision technology to allow shoppers to enter its stores, grab what they want and leave without having to scan items or use a payment method. The AI detects which items have been taken by the shopper and the system charges their Amazon account.
Computer vision can also power effective inventory management, as the technology is able to identify the number of items or crates in an image or video, saving the human worker from having to conduct a manual count. These automated inventory cycle counts provide retail workers with real-time updates, allowing them to make informed decisions regarding stock levels. It’s no surprise, then, that 64% of retailers plan to deploy data-driven solutions such as computer vision to optimize inventory management over the coming years.
Computer vision has a wide range of use cases for the automotive industry. For example, it can be used for inspection during the production process to detect flaws, which helps ensure quality standards are being met. Cameras placed over the production line can detect these defects and alert the manufacturing workers in real time. In fact, in one study, computer vision algorithms were able to detect faults in brake parts with an accuracy of 95.6%.
Computer vision is also an integral element of autonomous vehicles today. The technology can be used to recognize objects on the road, create 3-D maps, detect lane lines and drive in low light. Electric car manufacturer Tesla announced in 2021 that it will be relying exclusively on computer vision rather than lidar and radar for its new cars. The company’s chief AI scientist stated that the deep learning system is "a hundred times better than the radar."
Thanks to its ability to help drive efficiencies, save time and resources, enhance accuracy and outcomes and improve safety, computer vision technologies will likely see further adoption in the years to come. Businesses across industries should seek a reliable technology partner to support them in this process and ensure AI project success.