论文标题
学习筛分：从混凝土骨料的图像中预测分级曲线
Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate
论文作者
论文摘要
建筑物材料混凝土的大部分由骨料组成，其粒径在0.125至32毫米之间。其实际尺寸分布显着影响最终混凝土的质量特征，即新鲜和硬化状态。通常在使用回收骨料材料的情况下，通常不知道骨料颗粒的尺寸分布的变化通常是通过水泥使用增加来补偿的，但是，水泥的使用情况增加，这对混凝土生产的经济和生态方面产生了严重的负面影响。为了精确控制混凝土的目标特性，必须量化尺寸分布的未知变化，以实时对混凝土混合物设计进行适当的适应。为此，本文提出了一种基于深度学习的方法，用于确定具体骨料曲线。在这种情况下，我们提出了一个应用多尺度特征提取模块的网络体系结构，以处理粒子的强大物体大小。此外，我们提出并发布了用于定量评估我们方法的具体骨料的新型数据集。
A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects the quality characteristics of the final concrete in both, the fresh and hardened states. The usually unknown variations in the size distribution of the aggregate particles, which can be large especially when using recycled aggregate materials, are typically compensated by an increased usage of cement which, however, has severe negative impacts on economical and ecological aspects of the concrete production. In order to allow a precise control of the target properties of the concrete, unknown variations in the size distribution have to be quantified to enable a proper adaptation of the concrete's mixture design in real time. To this end, this paper proposes a deep learning based method for the determination of concrete aggregate grading curves. In this context, we propose a network architecture applying multi-scale feature extraction modules in order to handle the strongly diverse object sizes of the particles. Furthermore, we propose and publish a novel dataset of concrete aggregate used for the quantitative evaluation of our method.
