  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.
