Today, manufacturing is becoming more complex, as well as more automated. The industrial Internet of Things is generating great volumes of data at incredible speed, forming foundation of big data for manufacturing industry. Our R&D team works on a number of solutions that use modern computer vision and machine-learning techniques to increase speed of manufacturing processes, improve reliability, and make forecasting models based on sophisticated data analysis.

The transportation industry covers air, roads, railroads, and ocean lines. The future of transportation is increasingly reliant on advances in computer vision and smart systems. Our team works on a number of applications in the transportation industry that employ computer vision and machine learning technologies. We are excited about the fact that our expertise helps in the development of smart transportation. Our smart solutions bring a new level of comfort and improved travel safety to ordinary people, as well as reduce costs for companies and authorities in charge of transport infrastructure.

The retail industry has learned to collect a vast amount of data: sales, prices, costs, logistical data, product-related data, consumer behavior and much more. All this is available for retailers now. The next step is to transform this data into valuable insight. Powerful tools like machine-learning technologies open up a new world that allows retailers to see beyond collected data. The ability to refine, understand and act upon key factors revolutionize how business is done and brings the whole industry to a new level.

Traditional agriculture practices entail common tasks such as soil preparation, planting, water and nutrient management, weeding, harvesting, and sorting. Today with the advent of modern technologies, most processes can be successfully automated. From a technological point of view, computer vision and machine learning play a central role. For example, smart systems can sort yield, visually examining each single fruit as a human would do, but with incredible speed. Intelligent weeding systems recognize weeds by their shape and allow their elimination chemically or mechanically. Farm vehicles equipped with a computer vision navigation system can work in the field autonomously—in other words, with no driver.  In the end, all these systems enable farmers to significantly reduce costs and increase productivity at the same time.

The finance and insurance industry has been always one of the most intensively data-driven industries. With a large amount of diverse data and high demand on data analytics, the finance and insurance sectors have devoted attention to the rapidly developing area of machine learning. Today, machine learning offers the best opportunities in big data analysis. Using ML, finance institutions are able to reveal hidden dependencies and specified patterns among millions of records in a fraction of a second, detecting fraud, for example, or recognizing a trend.

Fortunately, these days we see continuous development of diagnostic and treatment tools available in hospitals and clinics. High-definition scanners allow radiologists to recognize anomalies more reliably and efficiently. However, the healthcare industry is always facing challenges. Our group works on solutions that help the whole industry take a big step forward: we work on automating the process of detecting the anomaly itself. Modern computer vision together with deep-learning models is already capable of seeing objects on radiology images and marking them out automatically. Such computer-aided diagnosis systems help doctors in analysis of medical images, increasing reliability and reducing workload.

Life sciences like other sciences heavily depend on routines such as counting, tracking, and classification. Today, many of these tasks can be successfully transferred to machine-learning and computer-vision systems. However, different studies and different objects either require adjustment of existing machine-learning models or tailor-developed solutions. BitRefine group offers tailor-made algorithms and is proud to take part in scientific research.