Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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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
    ;
    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
    A survey of the state of the art in sensor-based sorting technology and research
    ( 2024)
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Sensor-based sorting describes a family of systems that enable the removal of individual objects from a material stream. The technology is widely used in various industries such as agriculture, food, mining, and recycling. Examples of sorting tasks include the removal of fungus-infested grains, the enrichment of copper content in copper mining or the sorting of plastic waste according to the type of plastic. Sorting decisions are made based on information acquired by one or more sensors. A particular strength of the technology is the flexibility in sorting decisions, which is achieved by using various sensors and programming the data analysis. However, a comprehensive understanding of the process is necessary for the development of new sorting systems that can address previously unresolved tasks. This survey is aimed at innovative researchers and practitioners who are unfamiliar with sensor-based sorting or have only encountered certain aspects of the overall process. The references provided serve as starting points for further exploration of specific topics.
  • Publication
    GridSort: Image-based Optical Bulk Material Sorting Using Convolutional LSTMs
    ( 2023)
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Staab, Maximilian
    ;
    Pfaff, Florian
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    ;
    Kruggel-Emden, Harald
    ;
    Hanebeck, Uwe D.
    Optical sorters separate particles of different classes by first detecting them while they are transported, e.g., on a conveyor belt, and subsequently bursting out particles of undesired classes using compressed air nozzles. Currently, the most promising results are achieved by predictive tracking, a multitarget tracking approach based on extracted midpoints from area-scan camera images that analyzes the particles’ motion and activates the nozzles accordingly. However, predictive tracking requires expert knowledge for setup and preceding object detection. Moreover, particle shapes are only considered implicitly, and the need to solve an association problem rises the computational complexity of the algorithm. In this paper, we present GridSort, an image-based approach that forecasts the scene at the nozzle array using a convolutional long short-term memory neural network and subsequently extracts nozzle activations, thus circumventing the aforementioned weaknesses. We show how GridSort can be trained in an unsupervised fashion and evaluate it using a coupled discrete element–computational fluid dynamics simulation of an optical sorter. We compare our method with predictive tracking in terms of sorting accuracy and demonstrate that it is an easy-to-apply alternative while achieving state-of-the-art results.
  • 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
    Retroreflex ellipsometry for isotropic three-phase systems with nonplanar surfaces
    ( 2023)
    Chen, Chia Wei
    ;
    Hartrumpf, Matthias
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Ellipsometry is a long-established and highly-accurate method for characterizing materials and thin films in the semiconductor, optical coating and biology industry. However, the geometric shape of samples is usually limited to flat samples for conventional ellipsometers because the operation must fulfill the condition of the law of reflection. The retroreflex ellipsometer developed at Fraunhofer IOSB can overcome the shape constraints without realignment of the sample but information on surface inclinations is still necessary. In this work, the polarization model of nonplanar surfaces is analyzed by polarization ray tracing and a numerical inverse method is proposed to calculate the angle of incidence for isotropic three-phase systems with known refractive indices and ellipsometric parameters (Ψ,Δ). The experiments show excellent accuracy for the measurements of the incident and tilt angles and prove that the method is capable of measuring the film thickness of nonplanar surfaces.
  • Publication
    Benchmarking a DEM‐CFD Model of an Optical Belt Sorter by Experimental Comparison
    ( 2023)
    Bauer, Albert
    ;
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Kruggel-Emden, Harald
    ;
    Pfaff, Florian
    ;
    Gruna, Robin
    ;
    Hanebeck, Uwe
    ;
    Längle, Thomas
    A DEM-CFD (discrete element method - computational fluid dynamics) model of an optical belt sorter was extensively compared with experiments of a laboratory-scale sorter to assess the model's accuracy. Brick and sand-lime brick were considered as materials. First, the transport characteristics on the conveyor belt, involving mass flow, lateral particle distribution and proximity, were compared. Second, sorting results were benchmarked for varying mixture proportions at differing mass flows. It was found that the numerical model is able to reproduce the experimental results with high accuracy.
  • Publication
    Towards a feed material adaptive optical belt sorter: A simulation study utilizing a DEM-CFD approach
    ( 2022-09-09)
    Bauer, Albert
    ;
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Kruggel-Emden, Harald
    ;
    Pfaff, Florian
    ;
    Gruna, Robin
    ;
    Hanebeck, Uwe
    ;
    Längle, Thomas
    In this investigation, a DEM-CFD model of an optical belt sorter is modified to become adaptive to varying belt speeds. For that, the positions and orientations of the nozzle bar and collecting containers are rearranged. Also, the duration of nozzle activation and optimal position of particle ejection are adjusted. For the derivation of optimal velocity-dependent parameters, a two-dimensional model is derived and optimized as a pre-processing step. The derived parameters are applied to the three-dimensional DEM-CFD model. Two optically distinguishable types of demolition waste materials are considered. All conveyor belt velocities are investigated with instantaneously and lagged activated nozzles, which represent fast and realistic triggered nozzle activations. The application of optimized sorting setups shows promising sorting results for a broad range of conveyor belt velocities. The obtained results are discussed in terms of their feasibility in being applied to real optical belt sorters.
  • Publication
    Confocal fluorescence microscopy with high-NA diffractive lens arrays
    ( 2022)
    Li, Zheng
    ;
    Taphanel, Miro
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Traditionally, there is a trade-off between the numerical aperture and field of view for a microscope objective. Diffractive lens arrays (DLAs) with overlapping apertures are used to overcome such a problem. A spot array with an NA up to 0.83 and a pitch of 75 m is produced by the proposed DLA at a wavelength of 488 nm. By measurement of the fluorescence beads, the DLA-based confocal setup shows the capability of high-resolution measurement over an area of 3mm 3mm with a 2.5 0.07 NA objective. Further, the proposed fluorescence microscope is insensitive to optical aberrations, which has been demonstrated by imaging with a simple doublet lens.
  • 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).