Dr.-Ing.

Gruna, Robin

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  • Publication
    Automatic visual inspection based on trajectory data
    ( 2019)
    Maier, Georg
    ;
    Mürdter, N.
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Automatic inspection tasks have successfully been implemented in several industrial fields and are of growing importance. Visual inspection using optical sensors is wide spread due to the vast variety of different sensors, observable features and comparatively low prices. It seems obvious that corresponding systems are blind towards mechanical features and inspection of those typically requires highly specialized, inflexible and costly systems. Recently, we have shown in the context of sensor-based sorting that tracking objects over a time period allows deriving motion-based features which potentially enable discrimination of optically identical objects, although an optical sensor is used. In this paper, we take one step back from the specific application and study the classification of test objects based on their trajectories. The objects are observed while receiving a certain impulse. We further refrain from manually designing features but use raw coordinates as extracted from a series of images. The success of the method is demonstrated by discriminating spheres made of similar plastic types while bouncing off a plane.
  • Publication
    Application of area-scan sensors in sensor-based sorting
    ( 2018)
    Maier, Georg
    ;
    Pfaff, F.
    ;
    Pieper, C.
    ;
    Gruna, Robin
    ;
    Noack, B.
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    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.D.
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    Wirtz, S.
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    Scherer, V.
    ;
    Beyerer, Jürgen
    In the field of machine vision, sensor-based sorting is an important real-time application that enables the separation of a material feed into different classes. While state-of-the-art systems utilize scanning sensors such as line-scan cameras, advances in sensor technology have made application of area scanning sensors feasible. Provided a sufficiently high frame rate, objects can be observed at multiple points in time. By applying multiobject tracking, information about the objects contained in the material stream can be fused over time. Based on this information, our approach further allows predicting the position of each object for future points in time. While conventional systems typically apply a global, rather simple motion model, our approach includes an individual motion model for each object, which in turn allows estimating the point in time as well as the position when reaching the separation stage. In this contribution, we present results from our collaborative research project and summarize the present advances by discussing the potential of the application of area-scan sensors for sensor-based sorting. Among others, we introduce our simulation-driven approach and present results for physical separation efficiency for simulation-generated data, demonstrate the potential of using motion-based features for material classification and discuss real-time related challenges.
  • Publication
    Improving multitarget tracking using orientation estimates for sorting bulk materials
    ( 2017)
    Pfaff, F.
    ;
    Kurz, G.
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    Pieper, C.
    ;
    Maier, Georg
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    Noack, B.
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    Kruggel-Emden, H.
    ;
    Gruna, Robin
    ;
    Hanebeck, U.D.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Optical belt sorters can be used to sort a large variety of bulk materials. By the use of sophisticated algorithms, the performance of the complex machinery can be further improved. Recently, we have proposed an extension to industrial optical belt sorters that involves tracking the individual particles on the belt using an area scan camera. If the estimated behavior of the particles matches the true behavior, the reliability of the separation process can be improved. The approach relies on multitarget tracking using hard association decisions between the tracks and the measurements. In this paper, we propose to include the orientation in the assessment of the compatibility of a track and a measurement. This allows us to achieve more reliable associations, facilitating a higher accuracy of the tracking results.
  • Publication
    Numerical investigation of optical sorting using the discrete element method
    ( 2017)
    Pieper, C.
    ;
    Kruggel-Emden, H.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Pfaff, F.
    ;
    Noack, B.
    ;
    Hanebeck, U.D.
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Automated optical sorting systems are important devices in the growing field of bulk solids handling. The initial sorter calibration and the precise optical sorting of many materials is still very time consuming and difficult. A numerical model of an automated optical belt sorter is presented in this study. The sorter and particle interaction is described with the Discrete Element Method (DEM) while the separation phase is considered in a post processing step. Different operating parameters and their influence on sorting quality are investigated. In addition, two models for detecting and predicting the particle movement between the detection point and the separation step are presented and compared, namely a conventional line scan camera model and a new approach combining an area scan camera model with particle tracking.
  • Publication
    Improving material characterization in sensor-based sorting by utilizing motion information
    ( 2017)
    Maier, Georg
    ;
    Pfaff, F.
    ;
    Becker, F.
    ;
    Pieper, C.
    ;
    Gruna, Robin
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.D.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Beyerer, Jürgen
    Sensor-based sorting provides state-of-the-art solutions for sorting of cohesive, granular materials. Systems are tailored to a task at hand, for instance by means of sensors and implementation of data analysis. Conventional systems utilize scanning sensors which do not allow for extraction of motion related information of objects contained in a material feed. Recently, usage of area-scan cameras to overcome this disadvantage has been proposed. Multitarget tracking can then be used in order to accurately estimate the point in time and position at which any object will reach the separation stage. In this paper, utilizing motion information of objects which can be retrieved from multitarget tracking for the purpose of classification is proposed. Results show that corresponding features can significantly increase classification performance and eventually decrease the detection error of a sorting system.
  • Publication
    Sorting of black plastics using statistical pattern recognition on terahertz frequency domain data
    ( 2016)
    Brandt, Christian
    ;
    Kieninger, Michael
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    Negara, Christian
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Küter, A.
    ;
    Nüßler, Dirk
    The sorting of used plastics is an ever-growing market field which is further pushed by new EU regulations in, e.g., car recycling. Modern recycling techniques require pure or almost pure fractions of polymers. These pure fractions can be generated from waste using modern sorting technologies based on specific mechanical, electrical and chemical material properties such as density, conductivity and melting point. The thermal recycling of plastics is no longer seasonable. More modern recycling techniques require pure fractions containing only a single variety of polymer. A large portion of the plastic waste contains black or multilayer materials that are not sortable with todays' sorting technologies. To overcome this challenge, three Fraunhofer institutes are working together to develop a new type of sorting system. As a first step, we have developed a frequency domain line-scan camera working in the terahertz range with frequencies below 300 GHz. Since the entropy in terahertz signals below 300 GHz is not as high as needed for simple classification, more complex statistical pattern recognition methods are needed. The application of those methods to the problem of sorting black plastics as the second step in this joint project is presented in this paper. These methods have to be integrated into a real sorting system, which is the third part of our joint project. The modular approach gives the ability to integrate our sensors and algorithms into existing sorting systems.
  • Publication
    Simulation of an inverse schlieren image acquisition system for inspecting transparent objects
    ( 2016)
    Meyer, J.
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    This paper describes a novel approach for inspecting transparent objects. Since the underlying optical setup is based on a schlieren setup, any defect can be detected that leads to the deflection or extinction of incident light rays. By capturing a light field image of a defect-free transparent object and by illuminating objects under test with that very light field, the proposed inspection system can visualize defects by acquiring a single inspection image only. In order to evaluate the presented approach, a physically based rendering framework is used. It is extended by models and implementations of the necessary emitter and sensor plugins. Simulation experiments with virtual scenes that consist of a double-convex lens affected by different types of defects are the basis for a qualitative evaluation of the proposed method. The results show that a single image acquired with the described method is sufficient to test transparent objects for defects that cause the deflection or extinction of rays, e.g., enclosed absorbing or scattering impurities, shape anomalies, differences of the index of refraction and 3D-misalignments.
  • Publication
    Feature selection with a budget
    ( 2016)
    Richter, M.
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Feature selection is an important step in all practical applications of pattern recognition. As such, it is not surprising that during the past decades it has received a lot of attention from the research community. The topic is well understood and many methods have been put to the test. Most methods, however, overlook an aspect critical to real-time applications: limited computation time. The set of selected features must not only be suitable to solve the task, but must also ensure that the task can be solved within the available time. With this in mind, we propose a method for feature selection with a budget. We approach the problem by stating feature selection as a multi-objective optimization problem. This problem is solved using the well known NSGA-II algorithm. We evaluate our approach using one synthetic and two real-world datasets. We explore the properties of the genetic algorithm and investigate the classification performance of the resulting selections. Our results show that the selected feature sets are highly suitable, especially when considering real-time systems.
  • Publication
    TrackSort: Predictive tracking for sorting uncooperative bulk materials
    ( 2015)
    Pfaff, F.
    ;
    Baum, M.
    ;
    Noack, B.
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    Hanebeck, U.D.
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Optical belt sorters are a versatile, state-of-the art technology to sort bulk materials that are hard to sort based on only nonvisual properties. In this paper, we propose an extension to current optical belt sorters that involves replacing the line camera with an area camera to observe a wider field of view, allowing us to observe each particle over multiple time steps. By performing multitarget tracking, we are able to improve the prediction of each particle's movement and thus enhance the performance of the utilized separation mechanism. We show that our approach will allow belt sorters to handle new classes of bulk materials while improving cost efficiency. Furthermore, we lay out additional extensions that are made possible by our new paradigm.