Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

Filters

4
Reset filters

Settings
Now showing 1 - 10 of 4298
  • Patent
    Network node for a non-detectable laser communication system
    ( 2024-02-07)
    Kieleck, Christelle
    ;
    Eichhorn, Marc
    ;
    Rudow, Oliver
    ;
    Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.
    ;
    Hensoldt Sensors GmbH
    A network node (120) for a non-detectable laser communication system (100), wherein the laser communication system (100) is configured to send to the network node (120) at least one laser beam (10), comprises a reflector device (123), configured to generate, by a reflection of the laser beam (10), a reflected laser beam (20), and a modulator device (125), configured to provide a modulation of the reflected laser beam (20).
  • Publication
    Quantum computer-aided job scheduling for storage and retrieval systems
    ( 2024)
    Windmann, Stefan
    In this paper, a quantum computer-aided approach to job scheduling for automated storage and retrieval systems is introduced. The approach covers application cases, where various objects need to be transported between storage positions and the order of transport operations can be freely chosen. The objective of job scheduling is to arrange the transport operations in a sequence, where the cumulative costs of the transport operations and empty runs between subsequent transport operations are minimized. The scheduling problem is formulated as an asymmetric quadratic unconstrained binary optimization (QUBO) problem, in which the transport operations are modeled as nodes and empty runs are modeled as edges, with costs assigned to each node and each edge. An Quantum Approximate Optimization Algorithm (QAOA) is used to solve the QUBO. Evaluations of the quantum computer-aided job scheduling approach have been conducted on the IBM Q System One quantum computer in Ehningen. In particular, the running time for the solution of the QUBO has been investigated, as well as the scalability of the approach with respect to the required number of qubits.
  • Publication
    Enhancing Skeleton-Based Action Recognition in Real-World Scenarios Through Realistic Data Augmentation
    ( 2024)
    Cormier, Mickael
    ;
    Schmid, Yannik
    ;
    Beyerer, Jürgen
    Skeleton-based action recognition is a prominent research area that provides a concise representation of human motion. However, real-world scenarios pose challenges to the reliability of human pose estimation, which is fundamental to such recognition. The existing literature mainly focuses on laboratory experiments with near-perfect skeletons, and fails to address the complexities of the real world. To address this, we propose simple yet highly effective data augmentation techniques based on the observation of erroneous human pose estimation, which enhance state-of-the-art methods for real-world skeleton-based action recognition. These techniques yield significant improvements (up to +4.63 accuracy) on the widely used UAV Human Dataset, a benchmark for evaluating real-world action recognition. Experimental results demonstrate the effectiveness of our augmentation techniques in compensating for erroneous and noisy pose estimation, leading to significant improvements in action recognition accuracy. By bridging the gap between laboratory experiments and real-world scenarios, our work paves the way for more reliable and practical skeleton-based action recognition systems. To facilitate reproducibility and further development, the Skelbumentations library is released at https://github.com/MickaelCormier/Skelbumentations. This library provides the code implementation of our augmentation techniques, enabling researchers and practitioners to easily augment skeleton sequences and improve the performance of skeleton-based action recognition models in real-world applications.
  • Publication
    Tiled aperture coherent beam combination of 2 μm fiber lasers
    ( 2024)
    Romano, Clément
    ;
    Panitzek, Dieter
    ;
    Pradat-Peyre, Gabriel
    ;
    Milcent, Simon
    ;
    Uthayakumar, Jashaani
    ;
    Eichhorn, Marc
    ;
    Kieleck, Christelle
    Exceeding the multi-kW power level with thulium-doped fiber lasers has not been achieved using a single thulium-doped fiber laser. One solution to overcome this limit is the coherent beam combination. We focus on an active phase control with tiled aperture configuration. The setup consists in an amplified seed laser split in three channels. These channels are controlled in phase and amplified again before being launched free space and combined. A SPGD algorithm controls the channel’s phase to provide combination. Rise time below 0.5 ms were achieved with a residual amplitude noise lower than λ/30.
  • 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
    Bridging the Gap Between IDS and Industry 4.0 - Lessons Learned and Recommendations for the Future
    ( 2024)
    Alexopoulos, Kosmas
    ;
    Bakopoulos, Emmanouil
    ;
    Larrinaga Barrenechea, Felix
    ;
    Castellvi, Silvia
    ;
    Firouzi, Farshad
    ;
    Luca, Gabriele de
    ;
    Maló, Pedro
    ;
    Marguglio, Angelo
    ;
    Meléndez, Francisco
    ;
    Meyer, Tom
    ;
    Orio, Giovanni di
    ;
    Pethig, Florian
    ;
    Ruíz, Jesús
    ;
    Treichel, Tagline
    ;
    Usländer, Thomas
    ;
    Volz, Friedrich
    ;
    Watson, Kym
    ;
    Stojanovic, Ljiljana
    The Plattform Industrie 4.0 (PI4.0) and the International Data Spaces Association (IDSA) are two independent, parallel initiatives with clear focuses. While PI4.0 addresses communication and interaction between networked assets in a smart factory and/or supply chain across an asset or product lifecycle, IDSA is about a secure, sovereign system of data sharing in which all stakeholders can realize the full value of their data. Since data sharing between companies requires both interoperability and data sovereignty, the question emerges regarding the feasibility and rationality of integrating the expertise of PI4.0 and IDSA. The IDS-Industrial Community (IDS-I) is an extension of IDSA whose goal is to strengthen the cooperation between IDSA and PI4.0. Two fields of expertise could be combined: The Platform's know-how in the area of Industrie 4.0 (I4.0) and the IDSA's expertise in the areas of data sharing ecosystems and data sovereignty. In order to realize this vision, many aspects have to be taken into account, as there are discrepancies on multiple levels. Specifically, at the reference architecture level, we have the RAMI4.0 model on the PI4.0 side and the IDS Reference Architecture Model (IDS-RAM) on the IDSA side. While the existing I4.0 and IDS specifications are incompatible e.g. in terms of models (i.e., the AAS metamodel and the IDS information model) and APIs, there is also the issue of interoperability between I4.0 and IDS solutions. This position paper aims to bridge the gap between IDS and PI4.0 by not only analyzing how their existing concepts, tools, etc. have been "connected" in different contexts. Rather, this position paper makes recommendations on how different technologies could be combined in a generic way, independent of the concrete implementation of IDS and/or I4.0 relevant technology components. This paper could be used by both the IDS and I4.0 communities to further improve their specifications, which are still under development. The lessons learned and feedback from the initial joint use of technology components from both areas could provide concrete guidance on necessary improvements that could further strengthen or extend the specifications. Furthermore, it could help to promote the IDS architecture and specifications in the industrial production and smart manufacturing community and extend typical PI4.0 use cases to include data sovereignty by incorporating IDS aspects.
  • Publication
    The Drone-vs-Bird Detection Grand Challenge at ICASSP 2023: A Review of Methods and Results
    ( 2024)
    Coluccia, Angelo
    ;
    Fascista, Alessio
    ;
    Sommer, Lars Wilko
    ;
    Schumann, Arne
    ;
    Dimou, Anastasios
    ;
    Zarpalas, Dimitrios
    This paper presents the 6th edition of the Drone-vs-Bird detection challenge, jointly organized with the WOSDETC workshop within the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023. The main objective of the challenge is to advance the current state-of-the-art in detecting the presence of one or more Unmanned Aerial Vehicles (UAVs) in real video scenes, while facing challenging conditions such as moving cameras, disturbing environmental factors, and the presence of birds flying in the foreground. For this purpose, a video dataset was provided for training the proposed solutions, and a separate test dataset was released a few days before the challenge deadline to assess their performance. The dataset has continually expanded over consecutive installments of the Drone-vs-Bird challenge and remains openly available to the research community, for non-commercial purposes. The challenge attracted novel signal processing solutions, mainly based on deep learning algorithms. The paper illustrates the results achieved by the teams that successfully participated in the 2023 challenge, offering a concise overview of the state-of-the-art in the field of drone detection using video signal processing. Additionally, the paper provides valuable insights into potential directions for future research, building upon the main pros and limitations of the solutions presented by the participating teams.
  • Publication
  • Publication
    Evaluation of XAI Methods in a FinTech Context
    ( 2024)
    Gawantka, Falko
    ;
    Just, Franz
    ;
    Ullrich, Markus
    ;
    Savelyeva, Marina
    ;
    Lässig, Jörg
    As humans, we automate more and more critical areas of our lives while using machine learning algorithms to make autonomous decisions. For example, these algorithms may approve or reject job applications/loans. To ensure the fairness and reliability of the decision-making process, a validation is required. The solution for explaining the decision process of ML models is Explainable Artificial Intelligence (XAI). In this paper, we evaluate four different XAI approaches - LIME, SHAP, CIU, and Integrated Gradients (IG) - based on the similarity of their explanations. We compare their feature importance values (FIV) and rank the approaches from the most trustworthy to the least trustworthy. This ranking can serve as a specific fidelity measure of the explanations provided by the XAI methods.
  • Publication
    High-pulse-energy actively Q-switched Tm3+-doped photonic crystal fiber laser operating at 2050 nm with narrow linewidth
    ( 2024)
    Schneider, Julian
    ;
    Lassiette, Hugo
    ;
    Lorenz, Dominik
    ;
    Forster, Patrick
    ;
    Lautenschläger, Jan
    ;
    Panitzek, Dieter
    ;
    Romano, Clément
    ;
    Eichhorn, Marc
    ;
    Kieleck, Christelle
    An actively Q-switched diode-pumped Tm3+-doped fiber laser (TDFL) operating at 2050 nm is reported based on a flexible Photonic Crystal Fiber (PCF) with a core diamter of 50 μm. Using a fiber length of 3 m, the TDFL delivers gaussian shaped pulses with a maximum pulse energy of 1.5 mJ, corresponding to a peak power of 16 kW and a pulse width of 88 ns. The measured output spectrum shows a single peak at 2050 nm with a 3-dB-linewidth of 100 pm and 10-dB-linewidth of 270 pm. For a longer fiber length of 7 m, the effective gain is redshifted by reabsorbtion, increasing the achievable pulse energy up to 1.9 mJ. The average output power of the pulsed TDFL can be scaled to more than 100 W with a slope efficiency of 46 %. In all configurations the TDFL delivers nearly diffraction limited beam quality (M2 ⪅1.3).