发表机构
TU Darmstadt; TU Munich（达姆施塔特工业大学; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对工业中下落颗粒非接触式重量估计的需求，构建了名为Doppio的咖啡研磨下落颗粒视频数据集，评估多种深度学习模型，证明其可准确估计累积重量，为相关非接触式测量方案奠定基础。
AI 中文摘要
测量包括下落颗粒在内的粉末质量是工业应用中的常见任务。天平适用于静态测量，但许多应用需要非接触式传感，现有解决方案往往成本高、应用特定且技术复杂。本研究探索计算机视觉作为非接触式质量估计的实用替代方案，以咖啡研磨作为可访问的真实案例研究，引入新型视频数据集Doppio，该数据集捕获下落研磨咖啡的视频，并配有精确的逐帧真实重量测量值。为演示非接触式测量，评估从纯空间前馈网络到循环时空模型的多种深度学习方法，分析这些模型的预测准确性与计算权衡。研究表明，基于深度学习的计算机视觉模型可准确估计下落颗粒的累积重量，为未来基于视觉的非接触式测量解决方案奠定坚实基础。
英文摘要
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.
