Under pressure: learning-based analog gauge reading in the wild
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913314010824704 |
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| author | Reitsma, Maurits Keller, Julian Blomqvist, Kenneth Siegwart, Roland |
| author_facet | Reitsma, Maurits Keller, Julian Blomqvist, Kenneth Siegwart, Roland |
| contents | We propose an interpretable framework for reading analog gauges that is deployable on real world robotic systems. Our framework splits the reading task into distinct steps, such that we can detect potential failures at each step. Our system needs no prior knowledge of the type of gauge or the range of the scale and is able to extract the units used. We show that our gauge reading algorithm is able to extract readings with a relative reading error of less than 2%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08785 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Under pressure: learning-based analog gauge reading in the wild Reitsma, Maurits Keller, Julian Blomqvist, Kenneth Siegwart, Roland Computer Vision and Pattern Recognition Machine Learning Robotics We propose an interpretable framework for reading analog gauges that is deployable on real world robotic systems. Our framework splits the reading task into distinct steps, such that we can detect potential failures at each step. Our system needs no prior knowledge of the type of gauge or the range of the scale and is able to extract the units used. We show that our gauge reading algorithm is able to extract readings with a relative reading error of less than 2%. |
| title | Under pressure: learning-based analog gauge reading in the wild |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2404.08785 |