Intelligent systems must be able to reliably recognize people and objects from visual data taken from images or videos as well as develop an understanding of complex environments. This is a basic requirement if vehicles and mobile robots are to interact with their surroundings. Applying Artificial Intelligence to condition monitoring and quality control systems poses high demands on visual information processing.
We have many years experience of Machine Learning and image processing which gives us a competitive edge. Our systems have already been successfully put to the test in a range of applications and sectors and have proven themselves to work: For example, we offer solutions for recognizing road traffic signs, for condition monitoring systems in infrastructures, for damage detection and for quality analysis.
Our own core technology for image processing and neural network frameworks means we can recognize objects and analyze the surrounding area in real time. Data originating from different sources often needs to be combined and processed on distributed devices if a complex environment such as a specific traffic situation is to be analyzed.
These collaborative systems pose specific demands on resource efficiency and require special techniques for the fusion of data from different sensors. That is why we are working on and perfecting stand-alone Edge Computing systems and techniques which enable objects to be tracked through several fields of vision without involving a central cloud.
Traditional Machine Learning processes are often unable to recognize errors and quality defects in industrial production. By their very nature, errors and problems which only occur rarely can only be compared to a relatively small database. For application scenarios such as this we can develop hybrid processes and models which incorporate domain-specific expertise and can therefore also be trained to be used with smaller data sets.
Data assessment and improvement
We assess the reliability of image recognition, compensate for distortions and use data augmentation techniques to improve the base data.