Doppio: A Dataset for Contactless Weight Estimation of Falling Particles

2026-09-02

AI Summary

研究通过计算机视觉和深度学习方法，利用Doppio数据集解决了工业应用中粉末状落料无接触重量估计的问题。

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 often costly, application-specific, and technically complex. In this work, we investigate computer vision as a practical alternative for contactless mass estimation. As an accessible real-world case study, we focus on coffee grinding and introduce \emph{Doppio}, a novel video dataset capturing videos of falling ground coffee, paired with precise, per-frame ground-truth weight measurements. To demonstrate contactless measuring, we evaluate deep learning-based approaches ranging from purely spatial feed-forward networks to recurrent spatio-temporal models. These models are analyzed with respect to their predictive accuracy and computational trade-offs. We demonstrate that deep learning-based computer vision models accurately estimate the cumulative weight of falling particles, establishing a solid foundation for future vision-based contactless measurement solutions.

Problem

• contactless sensing
• mass estimation
• industrial applications

Innovation

• computer vision
• contactless weight estimation
• deep learning
• spatio-temporal models