Visual Material Characteristics Learning for Circular Healthcare

TL;DR

This paper advances circular healthcare by developing vision systems for resource mapping, waste sorting, and disassembly, supported by new datasets and demonstrating improved recovery processes through representation learning.

Contribution

It introduces novel vision systems for circular healthcare tasks and provides publicly available datasets and code to support further research.

Findings

Representation-learning vision improves recovery chain efficiency.

Autonomous systems are crucial for contamination risk management.

Published datasets facilitate research in disassembly operations.

Abstract

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.