Computer vision applications have become ubiquitous nowadays. It’s hard to think of a domain where the ability of computers to “see” what’s going on around them has not yet been leveraged. Delivering process automation and accuracy, computer vision technology is expected to have even greater momentum due to the COVID-19 pandemic as organizations have rushed to adopt automation on a larger scale. More nuanced use cases of computer vision in different industries are also predicted to emerge with the next evolutionary leaps in ; a field computer vision is a part of. Let’s look at the inroads already made by computer vision, its vast applications in different industries, and its benefits. artificial intelligence development What is computer vision, and how has it evolved? Computer vision (CV) is a that enables systems to interpret information from digital images and react to it with action or recommendations. The goal of computer vision technology is to emulate human vision for performing monotonous or complex visual tasks faster and even more efficiently. subset of AI , it all started with a lot of manual coding until machine learning progressed enough to allow developers to program smaller computer vision applications and apply statistical learning algorithms for specific CV-related tasks such as pattern recognition. With AI making major strides, ML algorithms have been largely replaced by deep learning or hybrid models that rely on neural networks to transform patterns into mathematical equations for information classification. After the launch of the first commercial software back in the 1970s, computer vision applications have evolved from enabling reading devices for the blind to transforming entire industries. Historically Some systems powered by computer vision have achieved 99% accuracy and can even surpass human performance (for instance, in ). diagnostic radiology The key drivers behind the surge in computer vision applications are: Spreading mobile technology that allows us to create and share billions of images a day Increasingly affordable computing power to process this massive amount of visual data New hardware A burst of deep learning algorithms What can computer vision do? — Major computer vision techniques With the growth in visual data and advances in computing power to process it, present-day computer vision applications rely on the following technology capabilities: to assign objects in a photograph or video to predefined classes. With binary classification, the system answers a simple question whether a particular object belongs to, say, a class of apples, tourist attractions, or cats. Object classification to locate an object in an image by enclosing it into a bounding box. Object localization to do both of the above to many objects in an image, assigning labels and localizing objects by drawing bounding boxes around them. Object detection to understand every pixel of an image and associate it with a class label (a car, a person, etc.) by creating object masks, with objects of the same class presented as a single entity. Semantic segmentation to do semantic segmentation and additionally identify different instances of the same class so that you don’t get just a one-color mask for three parked cars in a street view photo but label the vehicles with three different colors, identifying their boundaries. Instance segmentation Computer vision applications — Benefits and real-world examples Applications for computer vision have been expanding at a rapid pace over the last decade to reach a frantic level with the onset of the COVID-19 pandemic. Organizations now invest heavily in AI-driven solutions, from and to advanced manufacturing and government systems. Many are even concerned that AI technology is moving too fast for them, according to a retail software healthcare platforms survey of business leaders conducted by KPMG. While there is still an overall lack of AI regulation that comes with and bias risk, computer vision technology is a safer playing field due to its maturity. Given the investment speed and immediate benefits delivered by computer vision implementation, the worldwide market of computer vision solutions is to $19.1 billion from 2020 to 2027. AI explainability challenges projected to grow at a CAGR of 7.6% We’ve compiled a list of industries and use case examples to demonstrate how companies leverage advanced computer vision techniques to boost their results: Retail & Ecommerce Education Healthcare Fitness & Sports Precision Agriculture Manufacturing & Mining Cross-Industry Applications Computer vision applications in retail and eCommerce Benefits : Minimizing human interactions in stores to boost safety, deliver and lower labor costs digital shopping experience, Personalizing customer experience for increased engagement and more effective upselling and cross-selling strategies Leveraging next-gen in-store analytics to prevent stockouts, enhance store layout designs, and optimize staff scheduling Use cases: combine computer vision techniques with shelf sensors and deep learning to recognize shoppers, detect items they place into their carts, and automatically charge them on their accounts for items bought when they leave the store. Apart from the famous example, automated stores using sophisticated computer vision technology have also been launched by Chinese giants , , and . In the startup space, AI provider self-scanning scale solution can automatically identify fresh produce, allowing customers to check out without the need for barcodes or packaging. Automated checkouts and cashierless stores Amazon Go Lenovo JD Alibaba Tiliter’s powered by computer vision algorithms, can find the most efficient route to products on a customer’s in-app shopping list and recalculate the route if the person decides to look at other items. Currently, home improvement chain tests such an indoor mapping app developed with Google. Combining computer vision and the app reportedly locates users in an aisle more accurately than Wi-Fi or mobile phone systems while also providing access to product reviews. In-store navigation systems, Lowe’s augmented reality software, feature mobile barcode scanning software based on computer vision that recognizes objects for shoppers to get a personalized offer or information on the product in the store. Product information display apps Scandit’s that leverage deep learning algorithms make it more convenient for online shoppers to discover products, returning visually similar results. was among the first to offer its customers this engaging computer vision experience. Visual search solutions eBay are enabled by an array of computer vision-based systems. Among them are virtual mirrors that use augmented reality, allowing shoppers to try on various clothes virtually (like on site) or experiment with makeup products (like via app or the ). In-store virtual dressing room technology, embedded in Findmine’s solution, offers shoppers a touchscreen display with a CV-powered camera that recognizes items they are wearing to create an outfit based on images from the retailer’s catalog. Ecommerce sites are eyeing computer vision-enabled body scanning technology like the service to provide size or clothing recommendations. The skin care brand Neutrogena invites their customers to try the app that measures their skin health by assessing facial attributes and skin pixels and recommends a skin care routine. candy store uses facial recognition to identify regular shoppers for offering them personalized product recommendations or loyalty discounts. Personal recommendations Amazon’s Sephora’s Bourjois Magic Mirror Complete the Look Bodygram Skin360 The Lolli & Pops based on computer vision methods help retailers improve inventory management. Examples range from the technology that uses shelf-mounted computer vision cameras to alert staff about out-of-stock or incorrectly displayed items to the mobile robot that not only notifies shop assistants of gaps on shelves but also can detect damaged packaging and inaccurate pricing. Inspection systems Shelfie Tally use computer vision-powered cameras to analyze customer journeys and get product-related insights. Among them are famous pop-up store infused with capabilities to analyze dwell time, demographics, and other customer data, and Serbian fashion retailer store built in collaboration with Deloitte to gauge customer movement heatmaps. Connected stores Samsung’s Legend World Wide’s Computer vision applications in education Benefits: Understanding students’ learning behaviors to drive personalization and improve learning experiences Automating classroom monitoring to deter cheating in tests Assessing students’ papers to reduce the burden on educators Use cases: With computer vision-powered platforms, educators can measure students’ mood and behavior to capture signs of engagement or distraction both online and in a classroom. Solutions in this space include that uses facial recognition to analyze students’ emotional response to online content and the learning app that also reads facial expressions to detect frustration or boredom and adapt learning content. In addition, computer vision-driven insights help teachers regroup students into a more comfortable environment to improve learning. Student engagement detection and personalization. Emotuit Little Dragon has been made easy with computer vision-powered webcams that are used to identify students and flag cheating behaviors by tracking their postures or eye movements. Examples include with multi-factor authentication launched by the proctoring service and automated proctoring solutions like and . Attendance monitoring and automated online proctoring UAuto ProctorU Respondus Monitor Mettl is an area where computer vision is also expected to shine, as advanced algorithms are able to not only recognize responses written by students but also assist with their automatic evaluation. Handwritten character recognition Computer vision applications in healthcare Benefits: Improving patient identification to prevent wrong person procedures Delivering more accurate diagnosis through medical imaging analysis Providing assistance in surgery training and real-world surgeries for better outcomes Delivering rehabilitation assistance to patients Use cases: use computer vision-based cameras that help improve facial authentication of patients from check-in to discharge to prevent wrong person procedures. Patient identification systems assisted by computer vision is transforming radiology, helping practitioners interpret X-ray, CT scans, MRIs, and even microscopic images of cellular structures more accurately when diagnosing breast, brain, lung, or skin cancer. Computer vision applications in medical imaging also feature solutions to estimate a human pose in analyzing symptoms of neurological and neuropsychiatric disorders, monitor blood loss to optimize blood transfusions, diagnose eye conditions, and even detect COVID-19 (a deep convolutional neural network called has showed 90% accuracy in diagnosing COVID-19 based on chest X-ray images). Medical image analysis COVID-Net leverages computer vision technology to increase surgical precision. Apart from assistant surgical robot systems, there are solutions like that combine computer vision with ML and VR to create 3D visualizations for surgeons in the operating room. In the surgical training space, is a famous mobile simulator that provides a detailed guide to surgical procedures. Surgical simulation and assistance Proprio Vision Touch Surgery feature computer vision systems that are being developed to supervise exercise routines as part of at-home rehabilitation for rheumatoid arthritis, sports injury, brain injury, or stroke. Rehabilitation applications Computer vision applications in fitness and sports Benefits: Capturing performance data to aid coaches in training sessions and athletes in self-training Introducing advanced player or ball tracking methods to improve viewing experience or help referees in decision-making Collecting performance statistics for scouts, sports betting sites, and other industry professionals Use cases: powered by computer vision-enabled cameras detect and track moving players or balls in an array of games such as soccer, tennis, baseball or golf. Top examples include , designed by Sentio for soccer player tracking and analysis, and the optical tracking technology that gives football coaches a holistic view of matches. Computer vision-based systems are also used for improving shooting accuracy in basketball training ( ), help swimmers improve their techniques by collecting data from stroke rates to real-time velocity and turn times ( ), and even can take over in part the job of the umpire in professional tennis matches (the ball tracking solution). Tracking systems SentioScope SportVU 2.0 Noah System FINIS LaneVision Hawk-Eye that are based on computer vision techniques like pose estimation for motion analysis can help users improve their self-training, with , for instance, claiming that its fitness app powered by computer vision and ML can recognize yoga asanas with 95% accuracy. Such fitness solutions can also use markers as the intelligent fitness mirror does, unlocking data on user performance with the help of weight sensors to offer intelligent suggestions. Self-training solutions Zenia Carbon analyze the actions of players on the ice or field, producing meaningful insights either for building better game strategies or making smart decisions on players in the scouting market, or engaging viewers. Among famous examples are the system that collects raw data from video feeds to produce game models and the solution that can analyze any recorded basketball game for performance improvement and scouting insights. In-depth data analytics platforms Sportlogiq AutoSTATS Computer vision applications in precision agriculture Benefits: Identifying pests and weeds with greater accuracy to optimize the application of chemicals Monitoring crop development and the environment to maximize yields and produce better quality according to rising customer expectations Automating livestock management to prevent flock and herd losses and reduce the need for feet in the field Use cases: feature Blue River Technology’s y solution equipped with intelligent cameras that can distinguish between crops and weeds to apply herbicides to the right plants. Computer vision methods are also used for automatic identification and counting of flying insects and even for identifying apple diseases early. Pest and weed detection systems See & Spra are developed to detect the ripeness of fruit, including through color ratings of cherries in an outdoor environment or pick vegetables in a greenhouse using robots. Computer vision-aided monitoring solutions include , a 100% autonomous solar-powered drone system from XSun, that provides farmers with HD images capturing crop and soil conditions. AI solutions from Brazil’s can gauge information about the color, shape, and texture of crops. High-definition cameras from are equipped with sensors to help monitor soil moisture for yield prediction. Observation, harvesting, and prediction systems SolarXOne Cromai SWIR Vision Systems using drone technology can perform automatic counts, detect sick or injured animals, find strays, spot grazed areas, and even move cattle. A fair example is autonomous drone technology developed by Israeli firm to herd cattle. Livestock management systems BeeFree Agro Computer vision applications in manufacturing and mining Benefits: Implementing automated quality control to increase manufacturing accuracy, improve productivity, and produce better quality Deploying monitoring solutions to cut inspection time, minimize safety risks, improve operator productivity, and increase cost-efficiency Reduce human involvement to protect workers from hazardous environments Use cases: can be enabled by intelligent computer vision-powered cameras directed at a manufacturing line. Examples include machinery for the pharmaceutical industry to automatically count tablets or capsules on production lines and , a surface inspection system that identifies defects in items, stores images, and collects image-related metadata to classify errors by type and grade. Сomputer vision methods are also used to guide assembly operations, like assembly verification solutions from that measure product components versus manufacturing specifications, check caps and fill levels, and verify packaging components. Next-level quality control Pharma Packaging Systems’ WebSPECTOR Acquire Automation guided by machine vision automatically load or unload boxes and items to and from pallets. Robot palletizing systems assisted by computer vision technology and sensors have made it much easier to track the condition of critical infrastructure and determine when maintenance is needed. For example, FANUC’s takes photos and collects metadata to uncover any potential problems in the machine. Oil and gas giants such as Shell, ExxonMobil, and BP are using computer vision-powered predictive maintenance to anticipate failures in their equipment. Predictive maintenance systems Zero Down Time including drone-assisted systems, allow companies to conduct remote inspections of their sites and assets. This application of computer vision is especially important in mining, an unsafe industry for workers, where operators need to collect visual data in difficult areas. Visual inspections of well sites using the system, for instance, have enabled operators to reduce routine site visits by half. Intelligent monitoring solutions, Osprey Reach Computer vision in cross-industry applications systems using computer vision technology reduce inventory times from hours to minutes, delivering huge savings on operational costs. Such systems feature the computer vision-based platform that uses drones and connects to IoT devices for scanning and counting inventory. Another example is Amazon rolling out the robot technology at its sorting centers, claiming that the robot can improve sorting accuracy by 50%. Warehouse management Gather AI Pegasus enables a contactless delivery process for retail, logistics, post, and parcel businesses, transforming smartphones and other smart devices into computer vision-enabled barcode scanners like solutions from . The frictionless way to pick items from warehouses, distribution centers, or retail rooms is growing in popularity in the age of COVID-19. In retail, barcode scanning software is used for order fulfillment while allowing shoppers to safely collect items with one contactless mobile scan. Mobile computer vision Scandit using computer vision help keep public spaces safe during the pandemic by detecting ill employees or students as well as monitoring social distancing or exposure times. At construction sites and on the manufacturing floor, computer vision systems like monitor behavior-based safety and can protect people working around hazardous zones by alerting machine operators of dangerous events or incidents. Safety monitoring solutions IRIS What's next? It might be hard to believe, but we can uncover more computer vision applications and benefits with the advancement of technology such as edge computing, emotion AI, mixed reality, and embedded vision. And they can be quite incredible as artificial intelligence will get as sophisticated as we humans are. if you want to explore the benefits that computer vision can bring to your organization. Their AI experts will be happy to help you with complex or straightforward computer vision initiatives to address your specific business needs. Drop ITRex a line Previously published on https://itrexgroup.com/blog/computer-vision-applications-in-different-industries/

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Computer Vision Applications are Everywhere: Top Use cases in 2021

Computer Vision Applications are Everywhere: Top Use cases in 2021

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