mlsanityData to 7 Oct 2026

Visual Material Characteristics Learning for Circular Healthcare

arXiv 2309.04763 ↗First posted 9 Sep 2023cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 71 references that are in the map. First posted on arXiv 9 Sep 2023.

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18 of 71 references.

References · 18

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6 more
  1. 2019A Similarity Measure for Material Appearancecited by 19
  2. 2018Recognizing Material Properties from Imagescited by 14
  3. 2019Deep Learning Based Robot for Automatically Picking up Garbage on the Grasscited by 9
  4. 2022Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detectioncited by 9
  5. 2021Thermodynamical Material Networks for Modeling, Planning, and Control of Circular Material Flowscited by 4
  6. 2021Material Measurement Units for a Circular Economy: Foundations through a Reviewcited by 2

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