Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis
    ( 2023)
    Bäcker, Paul
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant analysis combined with a k-nearest neighbor classification is used. First results show a reliable detection of minerals and positive signs for separability of tar and bitumen. This work is a foundation for developing a sensor-based sorting system for physical separation of tar contaminated samples in road rubble.
  • 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
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    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
    Regression-based Age Prediction of Plastic Waste using Hyperspectral Imaging
    ( 2023)
    Kronenwett, Felix
    ;
    Klingenberg, Pia Charlotte Amaryllis
    ;
    Maier, Georg
    ;
    Längle, Thomas
    ;
    Metzsch-Zilligen, Elke
    ;
    Beyerer, Jürgen
    In order to enable high quality recycling of polypropylene (PP) plastic, additional classification and separation into the degree of degradation is necessary. In this study, different PP plastic samples were produced and degraded by multiple extrusion and thermal treatment. Using near infrared spectroscopy, the samples were examined and regression models were trained to predict the degree of aging. The models of the multiple extruded samples showed high accuracy, despite only minor spectral changes. The accuracy of the models of the thermally aged samples varied with the design of the training set due to the non-linear aging process, but showed sufficient accuracy in prediction.
  • Publication
    Sensitivity enhanced glucose sensing by return-path Mueller matrix ellipsometry
    ( 2023)
    Chen, Chia Wei
    ;
    Hartrumpf, Matthias
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Diabetes is a worldwide public health problem. According to the survey of the Robert Koch Institute, in Germany, at least 7.2 percent population (aged between 18 to 79 years) have diabetes. Therefore, the demand for glucose monitoring is increasing, especially for non-invasive glucose monitoring technology. In this work, we proposed a novel method to enhance the sensitivity of glucose monitoring by return-path ellipsometry with a quarter-wave plate and mirror. The coaxial design improves the sensitivity and reduces the complexity of optical system alignment by means of a fixed quarter-wave plate. The proposed system showed higher sensitivity compared to the transmission configuration.
  • Publication
    Machine learning-based multiobject tracking for sensor-based sorting
    ( 2022)
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Gruna, Robin
    ;
    Pfaff, Florian
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Hanebeck, Uwe
    ;
    Beyerer, Jürgen
  • Publication
    Sensitivity enhanced roll-angle sensor by means of a quarter-waveplate
    ( 2021)
    Chen, Chia-Wei
    ;
    Hartrumpf, Matthias
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Attitude metrology (roll, pitch, and yaw) playsan important role in many different fields. Roll angle is con-sidered the most difficult measurement quantity in angulardisplacements compared to pitch and yaw angles becausethe rotation axis of the roll angle is parallel to the probebeam. In this work, a sensitivity enhanced roll-angle sensor is presented. The principle is based on the polarizationchange of a sensing unit (quarter-waveplate). The polarization model is analyzed by Mueller matrix formalism. TheStokes parameters are detected by a Stokes polarimeter.The novel coaxial design improves the sensitivity and reduce the complexity of optical system alignment by meansof a fixed quarter-waveplate. The proposed sensor providesa simple setup to measure roll angles with a high sensitivity of 0.006∘ and a long unambiguous measurement range of 180∘.
  • Publication
  • Publication
    Direct-imaging DOEs for high-NA multi-spot confocal surface measurement
    ( 2021)
    Li, Zheng
    ;
    Taphanel, Miro
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Diffractive lens arrays with overlapping apertures can produce spot arrays with high numerical apertures (NAs). Combined with low-NA objectives, they can measure a large area with high lateral resolution. However, for surface measurements, the axial resolution of such setups is still fundamentally limited by the objectives. In this work, we propose a new design of diffractive optical elements (DOEs) to overcome this problem. The proposed Direct-imaging DOEs can perform 3D high-NA multi-spot surface measurements. Laterally, a non-vanishing contrast up to 1448 lp/mm is measured with a USAF resolution target. Axially, an average height of 917.5 nm with a standard deviation of 49.9 nm is measured with a calibrated step height target of 925.5 nm.
  • 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
    Light field illumination. Problem-specific lighting adjustment
    ( 2021)
    Kludt, Christian
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Choosing a proper lighting approach is a crucial task in designing visual inspection systems. It becomes especially challenging for complex-shaped, transparent objects, which change the directional distribution of incoming light in various ways. We overcome this challenge by constructing a light field display and deploy it as a highly tunable lighting device. Thereby, an object-specific light field can be generated, which highlights the features of the object under test with maximum contrast. We explain the calibration procedure, the rendering pipeline and present examples of customized illuminations.