Dr.-Ing.

Gruna, Robin

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  • Publication
    Handheld spectral sensing devices should not mislead consumers as far as non-authentic food is concerned: A case study with adulteration of milk powder
    ( 2022)
    Delatour, Thierry
    ;
    Becker, Florian
    ;
    Krause, Julius
    ;
    Romero, Roman
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Panchaud, Alexandre
    With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications. Full article(This article belongs to the Special Issue Rapid Detection Methods for Food Fraud and Food Contaminants Series II).
  • Publication
    Mixture of Experts of Neural Networks and Kalman Filters for Optical Belt Sorting
    ( 2022)
    Thumm, Jakob
    ;
    Reith-Braun, Marcel
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    Pfaff, Florian
    ;
    Hanebeck, Uwe D.
    ;
    Flitter, Merle
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Bauer, Albert
    ;
    Kruggel-Emden, Harald
    In optical sorting of bulk material, the composition of particles may frequently change. State-of-the-art sorting approaches rely on tuning physical models of the particle motion. The aim of this work is to increase the prediction accuracy in complex, fast-changing sorting scenarios with data-driven approaches. We propose two neural network (NN) experts for accurate prediction of a priori known particle types. To handle the large variety of particle types that can occur in real-world sorting scenarios, we introduce a simple but effective mixture of experts approach that combines NNs with hand-crafted motion models. Our new method not only improves the prediction accuracy for bulk material consisting of many particle classes, but also proves to be very adaptive and robust to new particle types.
  • 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
    Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking
    ( 2021)
    Maier, Georg
    ;
    Pfaff, Florian
    ;
    Pieper, Christoph
    ;
    Gruna, Robin
    ;
    Noack, Benjamin
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Hanebeck, Uwe D.
    ;
    Wirtz, Siegmar
    ;
    Scherer, Viktor
    ;
    Beyerer, Jürgen
    Sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this paper, we propose a new method for reliably separating particles at non-uniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.
  • Publication
    SmartSpectrometer - Embedded Optical Spectroscopy for Applications in Agriculture and Industry
    ( 2021)
    Krause, Julius
    ;
    Grüger, Heinrich
    ;
    Gebauer, Lucie
    ;
    Zheng, Xiaorong
    ;
    Knobbe, Jens
    ;
    Pügner, Tino
    ;
    Kicherer, Anna
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field.
  • 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
    An Extended Modular Processing Pipeline for Event-Based Vision in Automatic Visual Inspection
    ( 2021)
    Beck, Moritz
    ;
    Maier, Georg
    ;
    Flitter, Merle
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Heizmann, Michael
    ;
    Beyerer, Jürgen
    Dynamic Vision Sensors differ from conventional cameras in that only intensity changes of individual pixels are perceived and transmitted as an asynchronous stream instead of an entire frame. The technology promises, among other things, high temporal resolution and low latencies and data rates. While such sensors currently enjoy much scientific attention, there are only little publications on practical applications. One field of application that has hardly been considered so far, yet potentially fits well with the sensor principle due to its special properties, is automatic visual inspection. In this paper, we evaluate current state-of-the-art processing algorithms in this new application domain. We further propose an algorithmic approach for the identification of ideal time windows within an event stream for object classification. For the evaluation of our method, we acquire two novel datasets that contain typical visual inspection scenarios, i.e., the inspection of objects on a conveyor belt and during free fall. The success of our algorithmic extension for data processing is demonstrated on the basis of these new datasets by showing that classification accuracy of current algorithms is highly increased. By making our new datasets publicly available, we intend to stimulate further research on application of Dynamic Vision Sensors in machine vision applications.
  • 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
    From Visual Spectrum to Millimeter Wave: A Broad Spectrum of Solutions for Food Inspection
    ( 2020)
    Becker, Florian
    ;
    Schwabig, Christopher
    ;
    Krause, Julius
    ;
    Leuchs, Sven
    ;
    Krebs, Christian
    ;
    Gruna, Robin
    ;
    Kuter, Andries
    ;
    Längle, Thomas
    ;
    Nußler, Dirk
    ;
    Beyerer, Jürgen
    The consequences of food adulteration can be far reaching. In the past, inexpensive adulterants were used to inflate different products, leading to severe health issues. Contamination of food has many causes and can be physical(plant stems in tea), chemical (melamine in infant formula), or biological (bacterial contamination). Employing suitable sensor systems along the production process is a requirement for food safety. In this article, different approaches to food inspection are illustrated, and exemplary scenarios outline the potential of different sensor systems along the spectrum.