4 citations · 11 across the 4 of their papers we have counts for
4 papers · 1 filter
Image-based Deep Learning for the time-dependent prediction of fresh concrete properties
Max Meyer, Amadeus Langer, Max Mehltretter +5
Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO emissions that are associated with the produc…
ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles
Max Coenen, Tobias Schack, Dries Beyer +2
We present a semi-supervised method for panoptic segmentation based on ConsInstancy regularisation, a novel strategy for semi-supervised learning. It leverages completely unlabelle…
Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate
Max Coenen, Dries Beyer, Christian Heipke +1
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 th…
Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance
Max Coenen, Tobias Schack, Dries Beyer +2
In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to si…