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Computer vision dataset "Doppio" enables contactless weight estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a computer vision approach for contactless weight estimation of falling particles, using coffee grinding as a case study. They introduced "Doppio," a new dataset featuring videos of ground coffee with precise weight measurements. Deep learning models, including spatial feed-forward and recurrent spatio-temporal networks, were evaluated for their accuracy and computational efficiency in estimating cumulative weight, demonstrating the potential for vision-based contactless measurement solutions. AI

IMPACT This research could lead to more accessible and cost-effective contactless measurement solutions in industrial applications.

RANK_REASON The cluster describes a new dataset and research paper focused on a computer vision application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Computer vision dataset "Doppio" enables contactless weight estimation

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The cluster describes a new dataset and research paper focused on a computer vision application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Simon Kiefhaber, Jan-Martin O. Steitz, Julia Grabinski, Christoph Reich, Paul Wagner, Max Zimmermann, Simone Schaub-Meyer, Stefan Roth ·

    Doppio: A Dataset for Contactless Weight Estimation of Falling Particles

    arXiv:2609.02528v1 Announce Type: new Abstract: Measuring the mass of powder, including falling particles, is a common task in industrial applications. While scales are effective for static measurements, many applications require contactless sensing, where existing solutions are …

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