As business leaders investigate the transformative Return on Investment or ROI of computer vision, they are finding evidence that this technology can improve virtually every industry it touches. This explains the rapid growth of the computer vision market, which held an estimated value of $15.9 billion in 2021 and is expected to reach $51.3 billion by 2026.
It may explain why nearly all respondents in a recent IDG/Insight survey believe that computer vision will boost revenue while saving time and money—and this could also be why 37% of those respondents plan to implement the technology to improve their operations in the near future.
Whether the ROI goals of an organization relate to defect detection, operational efficiency, preventative maintenance, cost reduction, customer satisfaction, security, better healthcare outcomes, or safety—this white paper shows how computer vision is empowering firms across all industries to achieve never-before-possible outcomes.
This section describes what computer vision is, the basics of how it works, and the general benefits that enterprises receive when leveraging the technology.
In the simplest of terms, computer vision is a technology that allows computers to analyze, interpret, and understand visual information in order to automate tasks and make decisions that normally require humans. As IBM puts it, “If AI enables computers to think, computer vision enables them to see, observe, and understand.”
Of course, computers do not technically “see” the way humans do. Instead, they derive statistical inferences (or conclusions) from numerical values—values that represent the colors of pixels in a digital image.
Just like every person learns to see and understand the world, computer vision algorithms need to receive training to interpret and understand the visual information they capture. Computer vision platforms use deep learning technology to “train” algorithms capable of deriving inferences from visual data and making nuanced decisions based on those inferences.
The computer vision training process involves downloading thousands (or millions) of labeled or annotated images. For example, training images for a defect detection system would include annotations to indicate the presence/non-presence of defects and the types of defects involved. After receiving enough examples, the AI algorithm learns to identify these defects in real-time.
With the right training, an AI algorithm can perform a wide range of tasks that would have required visual monitoring, analysis, interpretation—even expert-level decision-making.
The best computer vision solutions function as full-lifecycle platforms that facilitate the fast and rapid training of new AI algorithms. A full-lifecycle computer vision platform includes tools to automate the process of annotating and tagging training images. Advanced platforms also feature tools to instantly generate synthetic training images, to increase the size of the datasets. This speeds up the process of developing computer vision models for unique and entirely novel scenarios.
Computer vision platforms may now feature no-code interfaces so non-tech savvy users can train sophisticated algorithms without knowing how to code.
A computer vision platform integrates with a network of high-definition cameras strategically positioned to capture visual information. This could be an existing network of cameras or a new one to suit the demands of the use case. Computer vision algorithms can even receive and interpret live video and images from mobile cameras, such as flying drones and walking robot dogs.
As for hosting, computer vision platforms can run in the cloud or on locally installed edge servers. Edge servers offer an increased level of security by hosting the data locally to the device. Developers can configure an edge-based computer vision system to integrate with existing IoT infrastructures and on-premises configurations.
The practical use cases for computer vision are endless, including systems for:
Ultimately, computer vision eliminates the errors, inefficiencies, and negative outcomes that inevitably happen when workers suffer from boredom or distractions in monotonous visual jobs. These negative outcomes include increased absenteeism, high turnover, errors, injuries, and counterproductive work behavior. (4) Computer vision also saves time and money through its capacity for 24/7 uptime and exponentially faster and more accurate visual task completion.
By freeing up employees to focus on more business-critical tasks, computer vision alleviates labor burdens—even when it comes to high-skilled analyst jobs. This brings the added benefit of decreasing the need for workers to be physically present during pandemic conditions.
Here is an overview of some of the most compelling ROIs of computer vision:
This section examines the return on investment for computer vision technology in specific industries. Since computer vision is a horizontal solution that services a wide variety of needs, there is significant overlap across the industries below, so it is worthwhile to read all of the sections.
Manufacturers are using computer vision to realize high ROIs in:
Manufacturing defects result in numerous costs related to the notification of customers/retailers, tracking down defective products, shipping costs, replacement costs, in addition to reputational damage, and product liability lawsuits. Human-led quality assurance activities are vital to preventing these costs, but the work is intensive, monotonous, expensive, and highly prone to errors. In other words, serious and potentially dangerous defects frequently go unnoticed due to unavoidable human error.
In contrast, computer vision automates real-time defect detection tasks to achieve orders of magnitude greater consistency, accuracy, and speed—at a cost that is exponentially more affordable than human labor alone. Computer vision can instantly detect scratches, cracks, dust, painting errors, dents, packaging errors, labeling errors, misplaced bottle caps, sanitary issues, and other defects that it is trained to identify.
Digital twins are virtual replicas of physical products and manufacturing processes—three-dimensionally digitized into the metaverse to help engineers understand, forecast, design, test, and optimize performance in real-time.
Computer vision assists in the creation of digital twins by using HD cameras and other sensors to capture the data required for rapid digital twin modeling and creation. Computer vision also allows digital twins to reflect real-time metrics, insights, and visuals on manufacturing activities as they happen.
Digital twin strategies with computer vision offer:
Manufacturers leveraging digital twin technology can achieve the following ROIs:
The severe injuries that arise from PPE non-compliance have been studied closely in the several industries. The statistics from NYU’s protective equipment standard illuminate the clear relationship between serious injuries and failure to use PPE. For example, only 1% of face injuries happen to people with facial protection. And, only 16% of workers who suffered head injuries were using hard hats (dispite 40% were required to wear them at the time of injury).
Strict enforcement of PPE rules is one of the most important ways to prevent these severe manufacturing injuries. Nevertheless, it is impossible for human managers to monitor PPE use for all workers across every inch of a facility at all times.
This is where computer vision can help. These systems automate PPE compliance monitoring for gloves, goggles, face shields, aprons, harnesses, hardhats, and more. They can identify all workers without PPE as soon as the compliance failure happens—sending real-time alerts to shift managers.
By boosting PPE compliance through improved monitoring, computer vision offers the high ROI of significantly reducing the number and severity of serious workplace accidents and injuries.
The above ROIs are only a few ways computer vision benefits manufacturing. Here is a summary of the most important computer vision ROIs for manufacturing, including some that have not been mentioned yet:
In addition to some of the ROIs referenced above, energy firms are using computer vision to optimize the following:
Successful monitoring of key infrastructure for predictive maintenance purposes is a high priority for large-scale energy operations. Unfortunately, human inspectors are prone to mistakes, misjudgments, and errors. It is also costly and dangerous to transport inspectors to remote locations, resulting in infrequent inspections.
In contrast, computer vision uses a network of cameras and sensors to inspect infrastructure components 24 hours a day—whether they are close by or in remote locations. Computer vision solutions can also deploy airborne inspection drones to monitor pipeline assets across large distances. With greater accuracy and cost-efficiency than human inspectors, these systems detect problems long before dangerous or costly incidents occur.
Deploying computer vision security solutions at remote oil and gas sites increases overall site security. The increased 24/7 monitoring that computer vision provides is also valuable at manned sites, especially after hours when workers are not present—or when it is challenging for security personnel to provide sufficient coverage.
Computer vision models for oil and gas site security achieve the following:
Oil and gas firms need to comply with strict rules from the Environmental Protection Agency (EPA); Environmental, Social, Governance (ESG); Socially Responsible Investing (SRI); and other organizations.
Nevertheless, unintentional violations of these environmental rules are common. In many cases, this is due to the difficulty in monitoring oil and gas sites in remote areas frequently. For example, the accidental discharge of untreated water into a waterway could continue for weeks or months before a human inspector arrives and takes notice.
By automating environmental law compliance monitoring at remote sites, computer vision offers 24/7 real-time alerts when the compliance issue appears. This empowers firms to fix environmental concerns before they result in costly damages, lawsuits, fines, and reputation damage.
The above ROIs are an example of the ways computer vision benefits the oil and gas industry. Below is a summary of the most important ROIs for oil and gas:
Similar to the industries above, computer vision offers clear ROIs for healthcare in the following areas:
Patient misidentification errors cost hospitals approximately $17.4 million in losses per year in denied insurance claims related to fatal and harmful injuries. Computer vision can dramatically reduce these errors—and reduce their damaging and costly consequences—by using real-time face identification to authenticate the identities of patients during every interaction with medical staff.
Computer vision platforms, such as Chooch, can also achieve HIPAA compliance and all seven levels of PACS integration to meet the strictest healthcare industry data privacy and data security requirements.
When doctors, radiologic technologists, and diagnosticians evaluate radiology images, the smallest oversight can result in a mistaken diagnosis. Unfortunately, these misdiagnoses are all too common—resulting in improper treatment protocols and poor patient outcomes.
To make matters worse, the U.S. Bureau of Labor Statistics predicts serious staffing shortages for radiologic and MRI technologists in the coming years, putting more stress on existing staff—and potentially increasing the chances of misdiagnosis.
Computer vision can relieve these staffing burdens while helping diagnosticians achieve more accurate results. In fact, a recent study in the journal Nature found that computer vision algorithms provided more accurate results than the average human reader (by an absolute margin of 11.5%) when analyzing mammography images for signs of breast cancer. When working with a human partner to provide “double-readings,” these AI systems reduced the workload of human readers by 88%.
In addition to the above study, another investigation showed that a medical AI neural network trained on images of lung X-rays was able to diagnose COVID-19 cases with 98% accuracy.
Computer vision for medical diagnosis frees radiologic technologists to dramatically improve the speed and accuracy of their work. This technology has the capacity to become an essential element of medical infrastructure in the years ahead.
The above ROIs are only a few of the ways computer vision benefits healthcare. Here is a summary of the most important ROIs for healthcare, including additional use cases that were not mentioned:
According to a 2020 survey, 59% of healthcare executives project that they will receive a full return on their medical AI investments in less than three years.
Retail stores are leveraging computer vision to take advantage of many of the ROIs mentioned above—in addition to reducing shrink, improving customer experiences, and optimizing their multichannel operations.
Here are three areas where retail stores are experiencing unique ROI advantages:
Out-of-stock items decrease sales, hurt consumer satisfaction and interfere with customer loyalty. According to Harvard Business Review, “stock-outs cause walk-outs.” 21-43% of customers will choose to shop somewhere else when they cannot readily find the items they want. More poignantly these abandoned sales cause annual losses of approximately 4% for retailers.
Computer vision solutions help reduce out-of-stock items by notifying retail employees when shelves are out of stock and need re-organizing/tidying. These systems also automate inventory reordering so key items are never out of stock.
Walmart has already experimented with computer vision strategies that use “shelf-scanning robots” that search for product supply irregularities. These AI-powered stock-out monitoring solutions offer better awareness of out-of-stock items while freeing up human employees to focus more strategically on restocking shelves and providing the best customer service.
he insights gained from tracking consumer behavior on smartphones have helped retailers dramatically improve the quality of their customers’ online experiences. Computer vision is now empowering a similar kind of revolution in customer experience improvement at physical stores. With computer vision’s capacity to track the detailed nuances of virtually any consumer behavior and interaction in retail stores, computer vision offers deep, real-time insights into the following:
With its revolutionary capacity to track and derive insights from minute customer behaviors, computer vision is empowering retailers to optimize customer interactions, customer satisfaction, and retail sales efficiency like never before.
Shoplifting and employee theft cause over 60% of inventory shrinkage in retail. One of the most common factors that lead to retail theft is a lack of restrictions on employee-only zones like stockrooms, offices, and break rooms—and/or the inability to monitor these areas and enforce the restrictions. Another common problem happens when check-out clerks aid shoplifters by purposefully failing to scan items.
Computer vision for retail loss prevention offers the following solutions:
The above ROIs are only a few of the ways computer vision benefits the retail industry. Here is a summary of the most important computer vision ROIs for retail, including additional use cases that were not mentioned:
Computer vision for retail achieves the following ROIs:
The above use cases for computer vision in retail yield higher profits, reduced inventory shrinkage, better customer behavior tracking, improved security, fewer labor costs, actionable data-driven insights, and dramatically greater process efficiency. In the years ahead, retailers can expect to see even more applications of this exciting technology in their industry.
Computer vision technology provides high business ROIs across many use cases and industries. Due to its capacity to achieve virtually any task that requires human eyes, human expertise, and human understanding—with greater speed, accuracy, consistency, cost efficiency than humans—this technology is radically transforming the ROI potential of every sector it touches. Now, computer vision solutions are rapidly deployable to businesses seeking exponential improvements in the areas of safety, security, quality assurance, patient outcomes, customer experience, industrial maintenance, and so much more.
At Chooch, we work closely with our ecosystem partners and customers to ensure high ROIs for each one of their computer vision initiatives. In the years ahead, as more businesses recognize the transformative power of computer vision, we look forward to helping firms across all industries—including manufacturing, energy, logistics, warehousing, retail, healthcare, construction, and other sectors—achieve exponentially better outcomes.
If you would like to learn about how computer vision can overcome unique challenges in your industry, contact our team and schedule a demo of the Chooch platform now.