Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    New active learning algorithms for near-infrared spectroscopy in agricultural applications
    ( 2021)
    Krause, Julius
    ;
    Günder, Maurice
    ;
    Schulz, Daniel
    ;
    Gruna, Robin
    The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.
  • Publication
    Predictive tracking with improved motion models for optical belt sorting
    ( 2020)
    Pfaff, Florian
    ;
    Pieper, Christoph
    ;
    Maier, Georg
    ;
    Noack, Benjamin
    ;
    Gruna, Robin
    ;
    Kruggel-Emden, Harald
    ;
    Hanebeck, Uwe D.
    ;
    Wirtz, Siegmar
    ;
    Scherer, Viktor
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Optical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type.
  • Publication
    Characterizing material flow in sensor-based sorting systems using an instrumented particle
    ( 2020)
    Maier, Georg
    ;
    Pfaff, Florian
    ;
    Bittner, Andrea
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    ;
    Noack, Benjamin
    ;
    Kruggel-Emden, Harald
    ;
    Hanebeck, Uwe D.
    Sensor-based sorting is a well-established single particle separation technology. It has found wide application as a quality assurance and control approach in food processing, mining, and recycling. In order to assure high sorting quality, a high degree of control of the motion of individual particles contained in the material stream is required. Several system designs, which are tailored to a sorting task at hand, exist. However, the suitability of a design for a sorting task is assessed by empirical observation. The required thorough experimentation is very time consuming and labor intensive. In this paper, we propose an instrumented bulk material particle for the characterization of motion behavior of the material stream in sensor-based sorting systems. We present a hardware setup including a 9-axis absolute orientation sensor that is used for data acquisition on an experimental sorting system. The presented results show that further processing of this data yields meaningful features of the motion behavior. As an example, we acquire and process data from an experimental sorting system consisting of several submodules such as vibrating conveyor channels and a chute. It is shown that the data can be used to train a model which enables predicting the submodule of a sorting system from which an unknown data sample originates. To our best knowledge, this is the first time that this IIoT-based approach has been applied for the characterization of material flow properties in sensor-based sorting.
  • Publication
    Real-time motion prediction using the chromatic offset of line scan cameras
    ( 2017)
    Pfaff, F.
    ;
    Maier, Georg
    ;
    Aristov, M.
    ;
    Noack, B.
    ;
    Gruna, Robin
    ;
    Hanebeck, U.
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    ;
    Pieper, C.
    ;
    Kruggel-Emden, H.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    Auf dem heutigen Stand der Technik der optischen Schüttgutsortierung werden Zeilenkameras mit einfachen Annahmen über die Teilchenbewegung kombiniert, um eine Ausschleusung bestimmter Teilchen zu ermöglichen. Kürzlich haben wir einen experimentellen optischen Bandsortierer mit einer Flächenkamera ausgestattet und gezeigt, dass durch das Verfolgen der Teilchen des Schüttguts die Güte der Vorhersagen und somit auch der Ausschleusung verbessert werden kann. In dieser Arbeit nutzen wir den Farbversatz zwischen den Farbkanälen einer Farbzeilenkamera, um in Echtzeit Informationen über die Bewegung der Teilchen abzuleiten. Dieser Ansatz erlaubt es, die Vorhersagen heutiger optischer Bandsortierer zu verbessern, ohne dass deren Hardware dafür angepasst werden muss.