Few-view in situ flash x-ray high-speed computed tomography (HSCT) is a computed tomography (CT) technique used to investigate high-speed phenomena on the timescale of microseconds. The successful application to quantitatively analyze and characterize fragments formed during a 1000 m s−1 impact process onto a ceramic plate with a CT reconstruction from only six x-ray projections has been shown. The method delivers spatially resolved 3D information about the fragments at one point in time. This information is not (or only partially) accessible by alternative experimental methods. Therefore, quantifying the accuracy of the measured data is not directly possible. In order to estimate the precision of the method and the influence of different sources limiting accuracy, a simulation study consisting of 250 virtual experiments was carried out. The border conditions of the study are based on the actual experimental data from the six-view experiment. The results show that steel fragments with a diameter of about 8 mm (volume ~ 300 mm3, weight ~ 2.5 g) can be reconstructed with an averaged relative volume deviation of about 30%. For larger framents, the error reduces down to 10% relative average deviation. The spatial position of the center of mass can be determined with an averaged uncertainty of about 0.8 to 1.2 mm for most fragment sizes.

The ECSIT project analyses how innovative inspection technologies can lead to an enhanced container security and how these technologies can be embedded into a holistic concept. It has the goal to analyze the possibility and feasibility for 100% scanning of all containers which are shipped to US ports and to develop a concept for integrating necessary infrastructure. A key element of the entire concept is the scanning technology itself. MeV X-ray technology using a linear accelerator as radiation source provides the feasibility to visualize the content of a container without opening it. If a 2-D radiography is ambiguous, a 3-D evaluation of the respective location could be conducted. MeV X-ray computed tomography (CT) is such a method to provide 3-D information of the content of a container. In the context of ECSIT, Fraunhofer EZRT has developed the concept of such a continuative high energy X-ray scanning stage and evaluated its application to sea freight containers. In this paper different approaches for measuring a 3-D tomographic volume data set of objects which are very heavy and thus difficult to move in arbitrary directions will be discussed. Three different geometrical principles for data acquisition were evaluated: laminography, limited angle CT, and a gantry CT. The volume data sets were reconstructed by using a standard filtered back projection and different algebraic reconstruction techniques (ART). Real 3-D volume data of large objects measured with the set-up described above are presented. As test objects a real container packed with various typical goods like furniture or consumer electronics as well as simulated threats like a bomb mock-up was used

In search and rescue (SAR) missions every minute counts. Semi-collapsed buildings are among the difficult scenarios encountered by search and rescue teams. An UAV-based exploration system can provide crucial information on the accessibility of different sectors, hazards, and injured people. The research project “UAV-Rescue” aims to provide UAV-borne sensing and investigate the use of AI to support this powerful tool. The sensor suite contains a radar sensor for detecting people based on breath and pulse movement. A neural network interprets the extracted data to identify signs of human life and as such persons that need rescuing. We also fuse radar and lidar data to explore the environment of the UAV and obtain a robust basis for simultaneous localization and mapping even under restricted visibility conditions. Additionally, we plan to use AI to support the path planning of the drone taking the digital map as input. Furthermore, AI is leveraged to map intact and damaged building structures. Potentially hazardous gases common to urban settings are tracked. We fuse the acquired information into a model of the explored area with marked locations of potential hazards and people to be rescued. The project also addresses ethical and societal issues raised by the use of UAVs close to people as well as AI supported decision making. The talk will present the system concept including interfaces and sensor fusion approaches. We will show first results of a research project from static and dynamic measurement campaigns demonstrating the capability of radar and lidar based sensing in a complex urban environment.