用视频分析咖啡粉下落，无接触估算重量。
Doppio: A Dataset for Contactless Weight Estimation of Falling Particles
- 基于视觉的深度学习模型分析下落咖啡粉视频
- 实现每帧精确的累积重量估计，误差低
- 适合工业无接触称重场景研究者
测量粉末质量，尤其是下落颗粒的质量，在工业应用中十分常见。虽然称重秤对静态测量有效，但许多场景需要无接触感知，而现有方案往往成本高、专用性强且技术复杂。本文探索计算机视觉作为无接触质量估计的实用替代方案。以咖啡研磨为实际案例，提出新数据集 Doppio，包含下落咖啡粉的视频序列及每帧精确的地面真实重量标注。通过评估从纯空间前馈网络到时序递归模型的多种深度学习方法，分析其预测精度与计算开销的权衡。结果表明，基于深度学习的视觉模型能准确估计下落颗粒的累积质量，为未来基于视觉的无接触测量提供坚实基础。
原文摘要 · Abstract (English)
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.
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