Visual Material Characteristics Learning for Circular Healthcare
Federico Zocco Shahin Rahimifard
The linear take-make-dispose paradigm at the foundations of our traditional
economy is proving to be unsustainable due to waste pollution and material
supply uncertainties. Hence, increasing the circularity of material flows is
necessary. In this paper, we make a step towards circular healthcare by
developing several vision systems targeting three main circular economy tasks:
resources mapping and quantification, waste sorting, and disassembly. The
performance of our systems demonstrates that representation-learning vision can
improve the recovery chain, where autonomous systems are key enablers due to
the contamination risks. We also published two fully-annotated datasets for
image segmentation and for key-point tracking in disassembly operations of
inhalers and glucose meters. The datasets and source code are publicly
available.