Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • 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
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    Längle, Thomas
    ;
    Hanebeck, Uwe D.
    ;
    Wirtz, Siegmar
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    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
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    Grüger, Heinrich
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    Gebauer, Lucie
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    Zheng, Xiaorong
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    Knobbe, Jens
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    Pügner, Tino
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    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
    From Visual Spectrum to Millimeter Wave: A Broad Spectrum of Solutions for Food Inspection
    ( 2020)
    Becker, Florian
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    Schwabig, Christopher
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    Krause, Julius
    ;
    Leuchs, Sven
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    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.
  • Publication
    Comprehensive Analysis of Deep Learning-based Vehicle Detection in Aerial Images
    ( 2019)
    Sommer, L.
    ;
    Schuchert, Tobias
    ;
    Beyerer, Jürgen
    Vehicle detection in aerial images is a crucial image processing step for many applications like screening of large areas as used for surveillance, reconnaissance or rescue tasks. In recent years, several deep learning based frameworks have been proposed for object detection. However, these detectors were developed for datasets that considerably differ from aerial images. In this article, we systematically investigate the potential of Fast R-CNN and Faster R-CNN for aerial images, which achieve top performing results on common detection benchmark datasets. Therefore, the applicability of eight state-of-the-art object proposal methods used to generate a set of candidate regions and of both detectors is examined. Relevant adaptations to account for the characteristics of the aerial images are provided. To overcome the shortcomings of the original approach in case of handling small instances, we further propose our own networks that clearly outperform state-of-the-art methods for vehicle detection in aerial images. Furthermore, we analyze the impact of the different adaptations with respect to various ground sampling distances to provide a guideline for detecting small objects in aerial images. All experiments are performed on two publicly available datasets to account for differing characteristics such as varying object sizes, number of objects per image and varying backgrounds.