The machines that are powered by deep learning are capable of working like humans, sometimes with higher precision. Their neural networks trained on thousands, millions, or billions of images until they became experts at classifying. Does this seem like a fantasy to you? No, these are things that happen around us. Computer Vision Implementation into our lives captures new directions every year.

It is Already Around the Us or Computer Vision Implementation

There are endless choices of computer vision examples, which show all benefits and create new scaling opportunities for companies. CV has had active use for years and we can see how it works and the result of the technology’s implementation.

Retail Improving

• Cashless stores, automated scales, and product absence sensors – all optimize the work of staff and allow them to spend more time with customers. It is also a very good way to control processes like filling of shelves, volumes of product orders, and balances in the warehouse in a large hypermarket and therefore avoid losing profit.
• E-commerce has used customers’ previous experiences to create personalized offers, thereby encouraging them to buy more. Similar products and cross-selling work perfectly and at the same time make the customer feel appreciated and satisfied.

New Level in Education Sector

• Handwritten character recognition is an area where Computer Vision is also a very useful tool. As advanced algorithms, it will be able to recognize responses written by students and also assist with their automatic evaluation.
• With the help of Computer Vision, teachers can understand behavior and improve content based on students’ previous experiences. This will encourage studying the subject and the teacher can adjust the intensity of the lesson.
• Automating classroom monitoring to deter cheating in tests is already used in many cases, especially when we talk about distance learning. Computer Vision-powered webcams identify students’ cheating behaviors by tracking their postures or eye movements.

Healthcare Transformation with Computer Vision Implementation

• Сomputer vision technology helps practitioners interpret X-rays, CT scans, MRIs, and even microscopic images of cellular structures more accurately when diagnosing breast, brain, lung, or skin cancer. 
• Surgical modeling and 3D visualization in the operating room significantly help surgeons and contribute to increasing surgical precision.
• Rehabilitation applications that use Computer Vision give the ability to stay home and exercise in the right way with the doctor’s advice directly.

Computer Vision Implementation for Fitness and Sport

• In the sports area, Computer Vision has been actively used in the last couple of years. This allows trainers to use such features as performance control, intensity adjustment, and comparison of achieved results.
• Computer Vision systems are also used to track the players and the ball on the field so that the referee can make accurate conclusions from the statistics. They can analyze the actions of players on the ice or field, and have the advantage to create meaningful insights either for building better game strategies or making smart decisions on players.
• The company ITRex can already offer a mirror with a 3D camera to count the user’s reps and monitor forms. This will greatly simplify the life of athletes and people who set sports goals for themselves in the appropriate time frame.

Computer Vision Solutions for Unsafe Industry

• There is a surface inspection system that identifies defects in items on production lines based on Computer Vision technology. Next-level quality control is today enabled: the program stores images and collects image-related metadata to classify errors by type and grade.
• Intelligent monitoring solutions, 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.
• Predictive maintenance 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. Oil and gas giants such as Shell, ExxonMobil, and BP are using Computer Vision-powered predictive maintenance to anticipate failures in their equipment.
• Intelligent monitoring solutions, 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.

The Best Computer Vision Tools 

The tech industry has seen a massive increase, which has been accompanied by the development of numerous toolkits, platforms, frameworks, and software libraries. The list of the most popular and robust Computer Vision tools in 2022:

OpenCV is an open-source machine learning and computer vision software library. It’s a real-time computer vision library, used for face detection and recognition, red-eye removal, object identification, and extraction of 3D models of objects and offers access to more than 2,500 algorithms. It is used by international companies, including Google, Facebook, IBM, Toyota, Sony, Honda, and Microsoft.

TensorFlow – a software library for machine learning. It`s implementing machine learning platforms with a comprehensive set of tools, resources, and libraries. This tool is very useful for building and deploying applications related to computer vision that are powered by machine learning. Tensorflow, like OpenCV, supports various languages like Python, C, C++, Java, and JavaScript.

Keras is a Python-based open-source software library that acts as an interface for the machine learning platform TensorFlow. This tool provides multiple backend support. Easy-to-use Python library allows user-friendly and fast build a neural network model quickly.

BoofCV is a Java-based computer vision software that is specially written for real-time computer vision solutions. It is a complete library with all the basic and advanced features for developing a computer vision application. This tool is open-source and free to use for academic and commercial purposes.

DeepFace is currently the most popular open-source computer vision library for facial recognition with deep learning. It deals with image processing to perform face recognition, face verification, or real-time facial attribute analysis, that perform based on computer vision with Python. It’s also free and open-source, even for commercial use.

YOLO offers real-time object detection and is recognized as the fastest computer vision tool. It owes its speed to the application of a neural network to the complete image, which then partitions the image into grids. This tool is highly accurate, with minimal background errors.

Trends and Tendencies for 2023:

Computer Vision on the Edge

The term edge computing refers to a technology attached to where the data is generated, i.e., at the edge of the architecture: it allows data to be processed and analyzed where (or closer to where) it is collected, instead of the cloud or a data center. Edge computing architectures are implementing more and more because it solves the problems of network accessibility, bandwidth, and latency. 

This technology is especially popular for projects where real-time data processing is needed: self-driving cars, drones, etc. The model is particularly active in the field of health care to solve the problems of people with disabilities.

It is endless to count the areas of use of this technology, but the main thing is that we can receive much more from the edge than now, so a lot of people wait for amazing results.

Data-Centric Computer Vision

Computer Vision works very well in synergy with data. The first step to building AI models is gathering huge datasets for training. 

The more accurate and informative the examples are, the faster the model will work correctly.

Having an advantage over quantity, you can systematically change or enhance your datasets to improve the performance of the model and receive better results. In the model-centric approach, this time the model is fixed, and you only improve the data.

Speeding up Insurance Processes

Tokio Marine, an auto-insurer, created a system based on the CV that helps to analyze damaged vehicles quickly. Computer Vision and machine learning techniques using deep learning can simplify image recognition processes and automatize this area. It helps boost business growth with a high customer satisfaction rate.

To Sum Up

The area of Computer Vision gives us advantages in improving a lot of processes that need a fixed human gaze. The development of this technology has been going on for several years and will only continue to implement. Current Computer Vision applications like cancer detection, autonomous vehicles, and facial recognition make undeniable use of deep learning. The development of future Computer Vision algorithms opens multiple doors for its real-world applications. This technology is worthy of attention to be among the leaders of the future.

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