How five industries are using computer vision technologies

How five industries are using computer vision technologies

CEO, InstaMart.AIan upcoming top-tier artificial intelligence market.

Machine vision has been around for decades, but in recent years, this technology has been advancing by leaps and bounds. Computer vision is an artificial intelligence (AI) application that can understand and interpret images, identifying and reacting with near-perfect accuracy due to advances 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 commercial applications are growing. The size of the global computer vision market 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 are going to Let’s focus on five of them. the most promising sectors.

1. Energy and utility

In the energy and utility industry, computer vision is driving more efficient operations, improving safety, and helping prevent damaging accidents. For example, machine vision-driven image analyzes of power poles can detect pole defects that can spark and turn into fires. Thanks to predictive maintenance technologies that pinpoint these anomalies, utilities can make a decision about whether the defect needs immediate attention and prevent events as extreme as wildfires.

In addition to flaw detection, computer vision applications in the energy and utility industry also include workplace and site safety. Deep learning algorithms can detect violations of security protocols or work zone intrusions by analyzing video in real time and alerting staff members to danger.

2. Hospitality

The restaurant industry was one of the hardest hit by the pandemic and many establishments were forced to go digital and innovate to stay afloat. Increasingly, restaurant chains are embracing AI innovations to help them drive efficiency and minimize costs.

Computer vision technology has enabled 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-service restaurants minimize incorrect orders and improve operations. Meanwhile, another startup is leveraging computer vision to help restaurants speed up processes and measure customer experience. Companies use the technology to measure the amount of time they spend in self-service and waiting in the dining room, as well as to update their security systems.

3. Health

In recent years, the healthcare industry has been increasingly leveraging computer vision to improve patient outcomes and drive operational efficiency. One of the main applications of computer vision in health care is analyzing scan images, both to detect abnormalities in an individual and to identify patterns in thousands of scans that can inform doctors’ knowledge of a given condition. Computer vision can often pick up patterns that the human eye can’t.

In fact, results from a breast cancer screening study show that visual AI systems were more accurate than human radiologists when looking for signs of breast cancer on mammograms, reducing the number of false positives and false negatives. By augmenting their analytics with machine vision, human providers were able to reduce their workload by a staggering 88%.

And it’s not just scan analytics that computer vision can support healthcare outcomes. The technology is also being used to prevent accidents in the hospital. For example, a machine vision-powered camera can detect when a provider forgot to sterilize a tool or left a foreign object in a patient during surgery, and then notify them of the error.

4. Retail

In the retail sector, machine vision applications are really booming. For example, retailers can create heat maps and analyze footfall, providing insights into customer behavior in the store. This allows them to experiment with different marketing strategies to increase sales.

Amazon is a well-known retailer that uses advanced computer vision technology to let shoppers walk into its stores, grab what they want, and leave without having to scan items or use a payment method. The AI ​​detects which items the buyer has taken and the system charges their Amazon account.

Machine vision can also power effective inventory management, as the technology can identify the number of items or boxes in an image or video, saving the human worker from having to do a manual count. These automated inventory cycle counts provide retail workers with real-time updates, allowing them to make informed decisions regarding stock levels. So it’s no surprise that 64% of retailers plan to implement data-driven solutions, such as computer vision, to optimize inventory management in the coming years.

5. Automotive

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, helping to ensure that quality standards are met. Cameras placed above the production line can detect these defects and alert manufacturing workers in real time. In fact, in one study, machine vision algorithms were able to detect brake part failures with 95.6% accuracy.

Computer vision is also an integral element 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 exclusively on computer vision instead of lidar and radar for its new cars. The company’s chief artificial intelligence scientist claimed that the deep learning system is “a hundred times better than radar.”


Thanks to their ability to help drive efficiency, save time and resources, improve accuracy and results, and enhance safety, computer vision technologies are likely to see increased adoption in the years to come. Companies from all industries need to look for a reliable technology partner to support them in this process and ensure the success of the AI ​​project.

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