The human ability to interpret and respond to visual information has always been taken for granted. However, it turned out that replicating it into machines is a challenging endeavor that took decades of research to overcome. This is due to the vast amount of information in the visual world and the fact that we still have much to learn about how human vision works and how the brain processes visual information.
Although computer vision CV still isn’t as complicated as human vision, it has made significant progress and has become practical for various business applications.
In this article, we will explore the intricacies of CV and its use cases in the business world
What is Computer vision
Computer vision is the branch of computer science that allows computers to interpret and understand visual data from the world to replicate and, in some cases, exceed human capabilities.
Recently, there has been significant progress in this field due to the use of neural networks and the increase in computing power, data storage, and inexpensive high-quality cameras.
This technology works by analyzing the pixel data from cameras and using specialized algorithms to identify patterns and objects. These algorithms are trained using large amounts of sample images, and with progress in machine learning and cloud computing, this process has become increasingly automated, resulting in highly accurate computer vision models.
Computer vision technology is now able to perform a wide range of advanced tasks, such as asset monitoring, predictive maintenance, inventory management, disease prevention, and initiating actions or alerts based on input, allowing humans to focus on more valuable tasks.
The process of computer vision can be broken down into three stages:
- Capturing the image: where digital cameras produce a digital file of binary data
- Analyzing the image: where algorithms are used to identify the basic geometric elements of the image
- Understanding the image: where high-level algorithms make decisions based on the analyzed image.
Why is computer vision important?
Computer vision technology is becoming more and more popular as AI and IoT are implemented across different industries. These technologies allow data to be extracted from the environment through the use of various sensors that provide feedback on different data points such as temperature, proximity, vibration, and pressure. While other sensors like laser measurement and radar, LiDAR, and infrared systems have their specific advantages, Computer vision can give more detailed and nuanced information about the surrounding environment. This includes the ability to identify, classify, and react to various conditions, as well as infer data from obscured or hidden objects.
This can be particularly useful in situations like warehouse inventory management where a camera may only have a limited view, but with the help of computer vision’s 3D modeling capability based on product size and shelf depth, the system calculates the total number of items in a given space.
Additionally, computer vision can be used in combination with other sensors to gain a more thorough understanding and deeper insights in the context of product inspection, where the system that uses computer vision can not only detect defects but also trigger further diagnostic analysis using the sensors to pinpoint and locate the source of the malfunction.
“Why the Present is Ripe for Adoption of Computer Vision”
In recent years, there has been an upsurge in the number of AI and computer vision-related products, specifically those that utilize cloud-based technologies, frameworks, and microservices. This has made it simpler for data scientists with minimal experience to build and maintain machine learning models. In addition, advances in edge devices have made it possible for these models to operate without the need for cloud-based resources. Which resulted in more efficient, accurate, and cost-effective models. Moreover, the widespread turmoil caused by the COVID-19 pandemic in 2020 accelerated the pace of digital transformation across various industries and led to an obvious shift in perceptions about the importance of AI, automation, and IoT, resulting in an expansion in investments in these areas.
Computer Vision use cases in business
Use cases in energy and resources
The energy industry is heavily investing in computer vision technology as it has the potential to save time and money, according to research from Insight and IDG. One of the main use cases for this technology is employee safety, with 88% of those investing or planning to invest in computer vision exploring how it can be used for this purpose. By automating certain processes, computer vision can reduce human exposure to dangerous environments, such as inspecting pipelines or wind turbines. This can help to lower costs, reduce risk and human error, and enable early repairs of equipment. Computer vision has also been used to improve efficiency in tasks such as land surveys and equipment maintenance in mining operations. Additionally, in the quest to improve energy efficiency, computer vision is being utilized to analyze satellite imagery, monitor weather conditions, and improve the accuracy of power requirements estimates by region.
Use cases in manufacturing
Computer vision is becoming increasingly popular among manufacturing and production companies, with 78% of them investing or planning to invest in it. It provides many benefits such as reducing downtime, improving employee safety, reducing theft, and improving customer outcomes. In addition, it can be used to pull out employees from remote or high-risk environments and decrease the potential for human error. Moreover, it can help to improve predictive maintenance and create a safer working environment. All in all, computer vision is proving to be a valuable asset for manufacturers and production companies.
Use cases in retail
Retailers are turning to computer vision technology to maximize their inventory and lessen expenditure. By correlating inventory data with ERP systems, discrepancies can be identified and future purchasing decisions can be made with confidence. Moreover, shrinkage can be decreased by identifying valuable items and linking pricing to POS machines. Thermal cameras are also being employed to reduce losses and enhance food safety. Furthermore, computer vision can be utilized to notify staff of product spills, lengthy checkout lines, and curbside pickups, allowing them to act quickly and prioritize customer satisfaction. By establishing computer vision solutions, retailers are able to boost their profitability, product availability, and customer experience.|
Use cases in healthcare
Computer vision technology presents a variety of opportunities for healthcare, particularly in medical diagnostics for conditions such as cancer and heart disease. However, the potential harm caused by a misdiagnosis is a significant concern, making it necessary for stricter protocols to be put in place, such as more thorough training, tighter margins of error, and greater human involvement. To mitigate this risk, healthcare providers are exploring alternative, lower-risk applications of the technology to optimize processes and enhance patient care. One popular example is utilizing optical character recognition (OCR) to automate document processing, which can reduce administrative burdens and decrease errors while also allowing healthcare providers to spend more time with patients. Additionally, computer vision can be utilized to improve inventory management and guarantee that medical supplies are easily accessible. Moreover, it can also be used to enhance security by monitoring pharmaceuticals and controlling the spread of COVID-19. With the ongoing pandemic, computer vision has become increasingly valuable in detecting fever symptoms and promoting good hygiene practices.
Use cases in Agriculture
Computer vision technology can be used in the agriculture industry to improve crop production and reduce the use of herbicides. A demonstration at CES 2019 featured a semi-autonomous combine harvester that used AI and computer vision to analyze grain quality and find the best route through the crops. Additionally, computer vision can be used to identify weeds, allowing herbicides to be targeted directly at them, which could potentially reduce herbicide usage by 90%.
Use cases in transportation
Autonomous vehicles: Computer vision technology plays a vital role in the functioning of autonomous vehicles, as it allows the vehicle to perceive and understand its surroundings. Automotive companies such as Tesla, BMW, Volvo, and Audi make use of a combination of cameras, lidar, radar, and ultrasonic sensors to gather images and data from the environment. These tools help the vehicle to identify objects, lane markings, traffic signs, and signals, which in turn enables the vehicle to navigate safely on the road.
Parking and Traffic: Computer vision can improve parking operations by using Automatic Number Plate Recognition (ANPR) technology to grant access to specific or all vehicles in a ticketless car park. Additionally, it can keep track of parking occupancy and identify how long vehicles stay in certain parking spaces in real-time, speeding up payment transactions and applying different pricing within the parking lot. Furthermore, it can detect stolen or uninsured vehicles and prevent criminal activity. Additionally, computer vision can help manage traffic by monitoring and analyzing density in different areas and reduce safety risks by assessing road conditions and detecting defects.
“How to Select the Right Use Case for Your Business Needs”
When considering implementing computer vision to address business challenges, it’s essential to choose the right use case. Computer vision has a wide range of potential applications; however, to ensure maximum benefits and minimize potential harm, it’s important to pick a use case that has clear business value, is relatively simple and specific, has high-quality labeled data, and has strong executive support for responsible AI usage. It’s essential to have all four of these criteria met for a project to be successful. Lacking any one of these factors could lead to difficulties in delivering results and even a negative impact on human outcomes. To identify the best use case for computer vision in an organization, We should ask questions like:
- Is there value in the proposed use case?
- Is there enough accessible data?
- Is there enough support and sponsorship?
- Is it responsible for implementing this use case?
By choosing the right use case, we will ensure a steady flow of visible benefits and encourage future AI investments while expanding expertise and reusable methods. Conversely, projects that fail to deliver value will miss potential benefits and discourage future investment in AI.
Many organizations have come to realize the value that computer vision can bring to their business. Over 90% of organizations have acknowledged the potential benefits of computer vision technology. Even though there are some factors to consider when investing in this technology, the benefits of a successful implementation outweigh the costs. By using best practices and utilizing computer vision, organizations can improve their processes, increase their revenue and enhance the experiences of both employees and customers. Adopting computer vision early can give organizations a competitive edge as this technology continues to influence the marketplace.