Automatically recognizing and outlining structural components such as doors and windows can speed up the process of creating the 3D model of a building from point clouds and flat images.
Flair-tech and in2it have developed a deep learning-based tool for automatically outline and classify the structural components of a building.
Stay tuned to our blog to know more.
Deep learning is increasingly revolutionizing the industry. One of the many areas which can be successfully supported by AI techniques is quality control and fault detection.
Flair-tech has supported Qvision in developing a deep learning-aided system to check whether a spherical mechanical part is correctly glued to a surface.
Read more on our blog.
Accurate segmentation of building contours in vertical aerial images is a crucial process in digital elevation modeling. If carried out manually, the process is massively time consuming.
Flair-tech has supported Geoin to develop a deep learning model to automatically produce the semantic segmentation of a vertical aerial image.
Stay tuned to our blog to know more.
Accurate machinery monitoring, diagnostics and prognostics allow to predict and prevent failures and performance degradation of industrial plants and individual components.
Flair-tech has supported SINTechnology and University of Florence to develop PROGNOSIS, a customizable hardware and software platform, implementing time series forecasting and anomaly detection algorithms.
Stay tuned to our blog to know more about our latest projects.
In the last decade deep learning techniques have replaced classical computer vision algorithms in many tasks. Insurance is one of the many successful applications of deep learning in image recognition.
Flair-tech is supporting Exclusive Garage in designing a fully automatic car damage recognition system based on deep learning. The predictive model is trained to accurately detect and localize scratches and dents on a car’s body.
Stay tuned to our blog to know more.
The electric power consumption of a railway network is often available as an aggregate measurement, with no insight into the contribution of individual trains. On the other hand, on-board measurement devices available on the market do not provide a cost-efficient and scalable solution for large networks.
Flair-tech has supported NRG.it in developing a scalable, low-cost measurement system based on machine learning and GPS. The underlying predictive model is able to infer the consumption of individual trains of a railway network given their instant GPS coordinates and information on their paths.
Stay tuned to our blog to know more.
When people and objects have to be accurately located within indoor, narrow or irregularly shaped environments, GPS tracking is not a reliable option. On the other hand, sophisticated antenna-based locators are often costly black boxes, requiring heavy customization to be effective.
Flair-tech has supported Dotdotdot to design and develop a low-cost, easily customizable indoor positioning system, based on machine learning and Bluetooth signal. We have trained the underlying model to map the signal measured by a chain of receiver nodes to the location of a person/object within a complex environment.
Stay tuned to our blog to know more.
VERIMA is a software solution by Witapp that allows a surgical team to plan their surgery carefully. Via a mixed reality visor, CT body scans can be viewed and interacted with as 3D holograms.
The deep learning core of VERIMA, that Flair-tech has proudly engineered and trained, is able to process raw CT scans and automatically highlight bones, vessels and organs, exploiting state-of-the-art semantic segmentation techniques.
Read more on our blog.