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Towards Sim-to-Real...
Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset
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- Zhu, Xiaomeng (författare)
- KTH,Robotik, perception och lärande, RPL,Scania CV AB, Sweden ; KTH Royal Institute of Technology, Sweden
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- Bilal, Talha (författare)
- Scania CV AB, Sweden,Scania CV AB, Scania Cv Ab
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- Mårtensson, Pär (författare)
- Scania CV AB, Sweden,Scania CV AB, Scania Cv Ab
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- Hanson, Lars (författare)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,User Centred Product Design,University of Skövde, University of Skövde
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- Björkman, Mårten, 1970- (författare)
- KTH,Robotik, perception och lärande, RPL,KTH Royal Institute of Technology, Sweden
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- Maki, Atsuto (författare)
- KTH,Robotik, perception och lärande, RPL,KTH Royal Institute of Technology, Sweden
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(creator_code:org_t)
- IEEE, 2023
- 2023
- Engelska.
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Ingår i: Proceedings, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. - : IEEE. - 9798350302493 - 9798350302509 ; , s. 4454-4463, s. 4454-4463
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset † and code ‡ are publicly available.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Benchmarking
- Computer vision
- Deep neural networks
- Internet protocols
- Five state
- Industrial parts
- Industrial use case
- Performance
- Post-processing
- Randomisation
- Real-world image
- State of the art
- Synthetic data
- Synthetic datasets
- Classification (of information)
- User Centred Product Design
- Användarcentrerad produktdesign
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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