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Computer vision has been around for decades, but in recent years this technology has advanced by leaps and bounds. Computer vision is an application of artificial intelligence (AI) that can understand and interpret images, identify and respond with near-perfect precision, thanks to advancements 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 sophisticated, its business applications grow. The global computer vision market size was estimated at $9.45 billion in 2020 and is expected to reach $41.11 billion by 2030. Computer vision has applications in a wide variety of industries, but in this article we are going to zoom in on five of the most promising sectors.
1. Energy and utility
In the energy and utility sector, computer vision enables more efficient operations, improves safety and helps prevent harmful accidents. For example, computer vision-driven analysis of images of electrical poles can detect defects in the poles that can spark and turn into fire. Thanks to predictive maintenance technologies that signal these anomalies, utilities can then decide whether to fix the defect immediately and prevent events as extreme as wildfires.
In addition to defect detection, computer vision applications in the energy and utility sectors also include workplace and site safety. Deep learning algorithms can detect security protocol violations or work zone violations by analyzing videos in real time and alerting employees to the danger.
The restaurant industry has been one of the hardest hit by the pandemic, and many establishments have been forced to digitize and innovate to stay afloat. Restaurant chains are increasingly adopting AI innovations to increase efficiency and minimize costs.
Computer vision technology has enabled restaurants to reduce long customer wait times, optimize floor space utilization and even monitor mask compliance. For example, a startup uses computer vision technology to help fast-serve restaurants minimize incorrect orders and improve operations. Meanwhile, another startup uses computer vision to help restaurants speed up processes and evaluate the customer experience. Companies are using the technology to measure the amount of time spent in the drive-in and waiting in the dining room, and to upgrade their security systems.
In recent years, the healthcare industry has increasingly used computer vision to improve patient outcomes and increase operational efficiency. One of the primary uses of computer vision in healthcare is analyzing images from scans, both to detect abnormalities in a person and to identify patterns in thousands of scans that can inform physicians’ knowledge of a particular condition. Computer vision is often able to notice patterns that the human eye cannot pick up.
In fact, the results of a breast cancer screening study show that visual AI systems were more accurate than human radiologists at looking for signs of breast cancer in mammograms, reducing the number of false positives and false negatives. By extending their analysis to include computer vision, the human providers were able to reduce their workload by as much as 88%.
And it’s not just in scan analytics that computer vision can support healthcare outcomes. The technology is also used to prevent accidents in the hospital. For example, a camera powered by computer vision can detect when a healthcare provider has forgotten to sterilize a device or left a foreign object with a patient during surgery and then notify him of the error.
In retail, computer vision applications are really exploding. For example, retailers can create heatmaps and analyze visitor numbers, which provides insight into customer behavior in the store. This allows them to experiment with different merchandising strategies to increase sales.
Amazon is a well-known retailer that uses advanced computer vision technology to enable shoppers to enter its stores, grab what they want, and leave without having to scan items or use any payment method. The AI detects which items have been taken by the shopper and the system charges their Amazon account.
Computer vision can also enable effective inventory management, as the technology can identify the number of items or crates in an image or video, eliminating the need for the human worker to manually count. These automated inventory cycle counts provide store associates with real-time updates, enabling them to make informed decisions about inventory levels. So it’s no surprise that 64% of retailers plan to deploy data-driven solutions such as computer vision in the coming years to optimize inventory management.
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 defects, ensuring that quality standards are met. Cameras positioned above the production line can detect these defects and alert production workers in real time. In one study, computer vision algorithms were even able to detect errors in brake components with an accuracy of 95.6%.
Computer vision is also an integral part of autonomous vehicles today. The technology can be used to recognize objects on the road, create 3D maps, detect lane lines and drive in low light. Electric car maker Tesla announced in 2021 that it will rely solely on computer vision instead of lidar and radar for its new cars. The company’s chief AI scientist stated that its deep learning system is “a hundred times better than radar”.
With the ability to increase efficiency, save time and resources, improve accuracy and results, and improve security, computer vision technologies are likely to be further adopted in the coming years. Companies in different sectors need to find a reliable technology partner to support them in this process and ensure the success of AI projects.
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