Summary of Under Pressure: Learning-based Analog Gauge Reading in the Wild, by Maurits Reitsma et al.
Under pressure: learning-based analog gauge reading in the wild
by Maurits Reitsma, Julian Keller, Kenneth Blomqvist, Roland Siegwart
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for reading analog gauges is designed for deployment on real-world robotic systems, breaking down the task into distinct steps to detect potential failures. This system requires no prior knowledge of the gauge type or scale range, and can extract units used. The algorithm achieves a relative reading error of less than 2%. This interpretable framework has the potential to improve the reliability and accuracy of robotic systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We developed a way for robots to read analog gauges without knowing what kind of gauge it is or what numbers are on it. Our method works by breaking down the task into smaller steps, so if something goes wrong at any step, we can stop and fix it. We tested our system and found that it can accurately read analog gauges with an error rate of less than 2%. |
