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Computer Vision Value for Businesses: 10 Proven Use Cases
As of 2020, 3.2 billion photos and 720,000 hours of video were created and shared on the Internet daily. So the question is — what are the opportunities to use visual data for the enhanced functioning of your business? Whether your company operates in finance, medicine, pharmaceutics, or real estate, computer vision can help you acquire new insights into the market and gain a much-needed competitive edge amid stiff competition. Today, we will take a closer look exactly at this technology, and you will learn how computer vision can contribute to the growth of your business.
What is computer vision
Computer vision is a rapidly growing type of Artificial Intelligence (AI) that enables the extraction of valuable information from different visual inputs, including images and videos. It heavily relies on algorithms that transfer implicit information from the visual data into knowledge and uses image analysis for purposes you choose. As visual data sets continue to expand, computer vision drives better decision-making in various spheres, starting from pharmaceutics and real estate to manufacturing and sports. Computer vision applications can analyze visual data without human interference in mere seconds. For this reason, vision software makes it possible for businesses to find innovative solutions for specific challenges they face along the way.
Machine vision vs. computer vision
Making the difference between machine vision and computer vision will help you effectively implement AI vision into a business. In a nutshell, machine vision refers to a subcategory of computer vision as it strongly depends on specific hardware products — processors, sensors, and cameras. Robot guidance, process control, and automatic inspections are the processes that serve as its core. Industrial manufacturing is the leading field where machine vision allows companies to use image analysis to detect product defects and build assembly lines timely and accurately. Meanwhile, computer vision can integrate image analysis into production without the connection to specific cameras or sensors, in this way, learning from available data and building vision systems via machine learning. It provides computer vision solutions in a wide range of industries and cases, while machine vision applies primarily to manufacturing.
Introduction to computer vision
At the beginning of 2000s, Google scientists created neural networks for computers to learn how to find cats in YouTube videos, which is an exemplary case for computer vision use. Let’s analyze a similar task. Suppose a computer is to identify whether a human face appears in the video. In that case, it should analyze a vast range of images with and without human faces. When there is a limited dataset, the system can start looking for different patterns in visual inputs, distinguishing between facial features – eyes, brows, lips, or noses. Then, a system will decide whether an image contains any of these elements of a human face, consequently tagging it with metadata and identifying the correct answer. This process takes multiple iterations that increase the quality of decision-making.
Here comes deep learning, a technology that improves the automatic finding of helpful information from images without additional programming. Combining deep learning and neural networks, computer vision algorithms mimic human intelligence and biological vision. Ready to explore more? In the following section, we will discuss ten proven use cases of computer vision that can help you learn how to use computer vision and open new opportunities for your business growth.
Ten most popular use cases of computer vision
Grand View Research consulting firm predicts – the score of the computer vision market will continue to expand, with a compound annual growth rate reaching 7.3% from 2021 to 2028. This article will focus primarily on its most illustrious use cases, so you can apply new knowledge for business development today.
Natural language processing
AI is a powerful instrument for natural language processing (NLP), while computer vision allows translating texts from images, photos, or pictures with the help of optical character recognition. Google Translate app serves as an example of this technology use as its system is ready to identify patterns in the visual data and form words from this input on a customer’s request. The client should download a picture or take a photo for instant translation from a foreign language. The process of translation itself goes after the iteration of computer vision algorithms. It can be conducted online and offline, enabling more smooth and accurate communication in various circumstances, especially when a customer is time-constrained.
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At the same time, combining natural language processing, computer vision, and voice detection can greatly contribute to the quality of interactions with customers in a wide range of spheres. Vision software can help detect emotions and, therefore, deliver more personal interactions with clients. As a result, you will have an opportunity to critically assess negative feedback from clients and strategize your actions on the way to delivering a more customer-oriented experience. You can consider computer vision applications to be an integral instrument for reaching these goals.
Facial recognition is an indispensable part of authentication in retail, banking, online, and transportation services. One of its major purposes is to prevent long identification processes associated with various workplaces or transportation systems. A facial recognition platform provides secure and anonymous biometric infrastructure that makes it possible to use computer vision at ATMs, self-checkout points, or cash desks. Clients do not need to use ID cards or tickets to enter or leave specific places. Biometric authentication and face recognition systems also continue to expand in the corporate world, with both small- and large-scale companies choosing new technological solutions for their back-office dashboards. Interested in finding out more about biometric authentication? Discover more on a facial recognition platform.
Blood loss measurement and cancer detection
Health care specialists increasingly use computer vision for blood loss measurement. This technology is beneficial in cases of post-partum hemorrhaging during childbirth. Before the advancement of AI, it was difficult to accurately measure blood losses and adjust the treatment of women to specific conditions. Besides, computer vision proves to be an effective instrument for identifying healthy and cancerous tissue. As recent research suggests, it greatly boosts rates of breast cancer detection and decreases chances of medical errors. With the dynamic technological progress in mind, the utilization of computer vision will soon span other types of cancer, improving patients’ outcomes and enhancing the quality of medical care. Learn more on AI Race for Healthcare and Pharma.
Safety measures improvement
In the past, safety measures required the collection of information from previous accidents. This tendency made it more complex to prevent and predict injuries in a long-term perspective. Computer vision changes the very understanding of how safety programs can and should use data to avoid accidents. The technology utilizes leading indicators – data points that empower specialists to improve decision-making and avert hazards beforehand. Operating in real-time, computer vision systems have the necessary tools to save human lives and increase levels of safety in various settings, which also minimizes the risk of human errors.
Fake news and deepfake detection
With fake news sweeping across the media, computer vision is an effective tool for detecting misinformation and scam. It can play a key role in the prevention of manipulations and distortion of reality. A pixel-based analysis enables the AI to track the credibility of the resource and verify its argumentation. Bringing distortions and a particularly dangerous technology of deep fakes to the spotlight, AI vision protects the Internet users in their search for trustworthy materials. Yet, the progress in fake news identification is indispensable from the advancement of fake news itself.
A 2018 research drew attention to the fact that deepfake faces do not blink, which seemed to be a turning point in their detection. But it changed soon afterward, with deepfake faces being pictured with specific changes in their eyelids. This race within the technology will drive its dynamic growth in the future, which requires companies to respond to potential hazards swiftly. As deepfake can contribute to the complexity of phishing, additional cybersecurity solutions can protect the vulnerability of internal procedures, in this way, safeguarding the company’s operations and improving its potential for future growth.
Autonomous vehicles production
Recovering from the COVID-19 pandemics, the global autonomous car market is predicted to stabilize in its growth and eventually reach $37 billion in 2023. Computer vision refers to an essential technology for the large-scale production of autonomous vehicles that can make the market live up to high standards of road traffic safety. Enhancement of computer vision systems will bring opportunities for more accurate and fast identification of objects on the road, making it possible for autonomous vehicles to avoid accidents. As potential customers have concerns over road safety, computer vision algorithm software is bound to face new challenges and meet requirements for higher accuracy.
In 2017, Ford Motor shared its plans to invest $1 billion in an autonomous vehicle technology start-up, Argo AI. A five-year plan covered the advancement in computer vision algorithms tailor-made primarily for the future production of self-driving cars. The case of Ford Motor Company shows how rapid advancement in self-driving cars technology requires the company’s ability to adapt to the market conditions and use AI for its continuous market growth.
Digital marketing analytics
According to analytics by McKinsey & Company, AI technologies are estimated to generate $1.4 to $2.6 trillion of value in marketing and sales for international companies in the future. Computer vision is an integral part of the game as well. What are the strategies to effectively implement it in the business’ digital marketing? Firstly, computer vision applications can advance customer segmentation, with companies accessing extensive sets of online images and building target audience profiles without traditional demographic research. Secondly, the emergence of generative adversarial networks (GANs), a machine learning model based on the competition of two neural networks, opens innovative ways for more personalized advertisement placement. With the help of GANs and sentiment analysis, marketers can access the advantages of image and video generation, creating content that would help reach out to various target audiences. Although GANs technology is still developing, it can make digital marketing more accurate and effective.
Sports broadcasting and coaching
Computer vision applications can benefit different spheres in sports, starting from broadcasting to coaching. Vision software uses a set of technologies, particularly multi-camera ball tracking and vision-based tracking systems, to collect precise data about athletes’ or players’ motions. Computer vision enables cameras to locate overlays into the image, which allows TV viewers to have an accurate analysis of the action. Meanwhile, computer vision applications can provide necessary information for optimal predicted positions of players. This information is valuable for coaches when they strategize games, especially when it comes to team sports such as soccer or hockey. Besides, this technology enhances predictable decision-making in sports gambling. According to Acumen Research and Consulting, the market value of AI in sports will reach $3,555.9 million by 2027, which makes the development of computer vision projects a crucial element of business growth.
Customer experience improvement in real estate
Given the stiff market competition, it becomes increasingly challenging for companies in real estate to stand out. In this regard, computer vision is an instrument for providing a top-notch quality customer experience. Computer vision algorithms make it possible for companies to classify rooms and analyze room conditions automatically. Their use can generate the searchable metadata necessary for enhanced customer experience. Implementing vision software into the real estate business can eliminate the need to process extensive data sets manually. Thus, clients can spend less time searching for requested information when this data is translated into specific keywords or search parameters. They will have open access to precise and valuable information with the help of room condition analysis and object/room detection, which can also advance the excellence of customer experience.
Image analysis for pharmaceutics
Two major areas of computer vision applications in pharmaceutics refer to products’ image analysis and safety standards advancement. Image analysis serves as an in-line analytical tool for identifying defects of products during various stages, including crystallization, granulation, milling, mixing, tableting, film coating, and vitro dissolution testing. At the same time, machine vision and its monitoring systems can help the company ensure safety standards. Vision software can track whether employees adhere to personal protection equipment requirements. Besides, computer applications can detect when chemical spills occur, mitigating risks of employee injuries and making it possible to prevent similar situations in the future. In-process analysis of pharmaceutical substances can increase production capacities and regulate safety standards, making computer vision one of the cutting-edge technologies in pharmaceutics.
Seeing the future with computer vision
Computer vision becomes a daily operation across multiple business areas, from financial services, banking, insurance, pharma and life science to real estate and manufacturing. Spurred by a large influx of visual data, this technology makes it possible for the computer to filter out relevant information for different purposes. Deep learning and neural networks breathe life into this type of AI, empowering its ability to recognize specific patterns in images. With the help of cameras and algorithms, computer vision can mimic and supersede human sight. It greatly contributes to its potential to bring effective positive change in the future.
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