Dr.-Ing.

Gruna, Robin

Filters

3
4 1
Reset filters

Settings
Now showing 1 - 5 of 5
  • Publication
    Detection of pyrrolizidine alkaloid containing herbs using hyperspectral imaging in the short-wave infrared
    ( 2021)
    Krause, Julius
    ;
    Tron, Nanina
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Krähmer, Andrea
    Plants containing pyrrolizidine alkaloids (PA) are unwanted contaminants in consumer products such as herbal tea due to their toxicity to humans. The detection of these plants or their components using hyperspectral imaging was investigated, with focus on application in sensor-based sorting. For this, 431hyperspectral images of leafs from three common herbs (peppermint, lemon balm, stinging nettle) and the poisonous common groundsel were acquired. By using a convolutional neural network, a mean F1 score of 0.89 was obtained for the classification of all four plant products based on the individual spectra. To validate the neural network, significant wavelengths were determined and visualized in an attribution map.
  • Publication
    Motion-based visual inspection of optically indiscernible defects on the example of hazelnuts
    ( 2021)
    Maier, Georg
    ;
    Shevchyk, Anja
    ;
    Flitter, Merle
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Hanebeck, Uwe D.
    ;
    Beyerer, Jürgen
    Automatic quality control has long been an integral part of the processing of food and agricultural products. Visual inspection offers solutions for many issues in this context and can be employed in the form of sensor-based sorting to automatically remove foreign and low quality entities from a product stream. However, these methods are limited to defects that can be made visible by the employed sensor, which usually restricts the system to defects appearing on the surface. An alternative non-visual solution lies in impact-acoustic methods, which do not suffer from this constraint. However, these are strongly limited in terms of material throughput and consequently not suitable for large scale industrial application. In this paper, we present a novel approach that performs inspection based on optically acquired motion data. A high-speed camera captures image sequences of test objects during a transportation process on a chute with a specific structured surface. The trajectory data is then used to classify test objects based on their motion behavior. The approach is evaluated experimentally on the example of distinguishing defect-free hazelnuts from ones that suffer from insect damage. Results show that by merely utilizing the motion data, a recognition rate of up to for undamaged hazelnuts can be achieved. A major advantage of our approach is that it can be integrated in sensor-based sorting systems and is suitable for high throughput applications.
  • 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
    Application of area-scan sensors in sensor-based sorting
    ( 2018)
    Maier, Georg
    ;
    Pfaff, F.
    ;
    Pieper, C.
    ;
    Gruna, Robin
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.D.
    ;
    Wirtz, S.
    ;
    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 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.