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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Mikut, Ralf
Karlsruhe Institute of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2024Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction
- 2023Temperature-based quality analysis in ultrasonic welding of copper sheets with microstructural joint evaluation and machine learning methodscitations
- 2022Material matters: predicting the core hardness variance in industrialized case hardening of 18CrNi8 [Vorhersage der Kernhärtenvarianz von industriell einsatzgehärtetem 18CrNi8]
- 2021CAD-to-real: enabling deep neural networks for 3D pose estimation of electronic control unitscitations
- 2019Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization
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article
CAD-to-real: enabling deep neural networks for 3D pose estimation of electronic control units
Abstract
<jats:title>Abstract</jats:title><jats:p>Image processing techniques are widely used within automotive series production, including production of electronic control units (ECUs). Deep learning approaches have made rapid advances during the last years, but are not prominent in those industrial settings yet. One major obstacle is the lack of suitable training data. We adapt the recently developed method of domain randomization to our use case of 3D pose estimation of ECU housings. We create purely synthetic data with high visual diversity to train artificial neural networks (ANNs). This enables ANNs to estimate the 3D pose of a real sample part with high accuracy from a single low-resolution RGB image in a production-like setting. Requirements regarding measurement hardware are very low. Our entire setup is fully automated and can be transferred to related industrial use cases.</jats:p>