Dr.-Ing.

Gruna, Robin

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  • Publication
    Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning
    ( 2024)
    Kronenwett, Felix
    ;
    Maier, Georg
    ;
    Leiss, Norbert
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    Gruna, Robin
    ;
    Thome, Volker
    ;
    Längle, Thomas
    Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the basis for sorting. To enable economical use, it is necessary to ensure robust detection of individual objects even with high material throughput. Conventional algorithms struggle with resulting high occupancy densities and object overlap, making deep learning object detection methods more promising. In this study, different deep learning architectures for object detection (Region-based CNN/Region-based Convolutional Neural Network (Faster R-CNN), You only look once (YOLOv3), Single Shot MultiBox Detector (SSD)) are investigated with respect to their suitability for CDW characterization. A mixture of brick and sand-lime brick is considered as an exemplary waste stream. Particular attention is paid to detection performance with increasing occupancy density and particle overlap. A method for the generation of synthetic training images is presented, which avoids time-consuming manual labelling. By testing the models trained on synthetic data on real images, the success of the method is demonstrated. Requirements for synthetic training data composition, potential improvements and simplifications of different architecture approaches are discussed based on the characteristic of the detection task. In addition, the required inference time of the presented models is investigated to ensure their suitability for use under real-time conditions.
  • Publication
    Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting
    ( 2023)
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Gruna, Robin
    ;
    Pfaff, Florian
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Hanebeck, Uwe D.
    ;
    Beyerer, Jürgen
    Sensor-based sorting offers cutting-edge solutions for separating granular materials. The line-scanning sensors currently in use in such systems only produce a single observation of each object and no data on its movement. According to recent studies, using an area-scan camera has the potential to reduce both characterization and separation error in a sorting process. A predictive tracking approach based on Kalman filters makes it possible to estimate the followed paths and parametrize a unique motion model for each object using a multiobject tracking system. While earlier studies concentrated on physically-motivated motion models, it has been demonstrated that novel machine learning techniques produce predictions that are more accurate. In this paper, we describe the creation of a predictive tracking system based on neural networks. The new algorithm is applied to an experimental sorting system and to a numerical model of the sorter. Although the new approach does not yet fully reach the achieved sorting quality of the existing approaches, it allows the use of the general method without requiring expert knowledge or a fundamental understanding of the parameterization of the particle motion model.
  • 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
    Real-time multitarget tracking for sensor-based sorting: A new implementation of the auction algorithm for graphics processing units
    ( 2019)
    Maier, Georg
    ;
    Pfaff, F.
    ;
    Wagner, M.
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    Pieper, C.
    ;
    Gruna, Robin
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.D.
    ;
    Scherer, V.
    ;
    Beyerer, Jürgen
    Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes.
  • Publication
    Motion-based material characterization in sensor-based sorting
    ( 2018)
    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
    Für die Sortierung von kohäsiven, granularen Materialien entspricht die sensorgestützte Sortierung dem Stand der Technik. Die Auswahl geeigneter Systemkomponenten, wie etwa Sensorik, Beleuchtung, oder die Realisierung der Datenauswertung, orientiert sich bei der Entwicklung entsprechender Systeme an der konkreten Sortieraufgabe, die es zu lösen gilt. Eine Gemeinsamkeit findet sich im Einsatz scannender Sensoren. Jüngst wurde jedoch der Einsatz von Flächenkameras vorgeschlagen. Durch die Beobachtung von Objekten zu mehreren Zeitpunkten besteht die Möglichkeit, deren Bewegungspfade zu verfolgen. Dies erlaubt eine präzise Schätzung der Position und des Zeitpunkts, zu welchem ein Objekt die Trennstufe des Systems erreicht und hilft somit dabei, den Fehler in der physikalischen Separation zu verringern. In dieser Veröffentlichung wird vorgeschlagen, diese Bewegungsinformation ebenfalls zur Charakterisierung von Materialien zu verwenden. Durch die Ableitung geeigneter Merkmale zeigen wir exemplarisch für eine Klassifikationsaufgabe, dass hierdurch gute Ergebnisse erzielt werden können. Der vorgestellte Ansatz trägt damit zur Verringerung des Erkennungsfehlers in Sortiersystemen bei.
  • 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.