Visual Material Characteristics Learning for Circular Healthcare
This work addresses waste and supply issues in healthcare by enabling autonomous systems for circular economy tasks, though it appears incremental as it applies existing vision methods to a new domain.
The paper tackled the problem of increasing material circularity in healthcare by developing vision systems for resources mapping, waste sorting, and disassembly, resulting in performance improvements in the recovery chain and the release of two annotated datasets.
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.