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.

我们通过开发若干视觉系统，实现了资源映射和量化、废物分类和拆解等三个主要循环经济任务，从而向循环医疗迈出了一步。我们的研究表明，基于表示学习的视觉系统可以改善回收链，而自主系统是关键因素之一，因为它们可以降低污染风险。我们还公开了两个完整注释的数据集，用于吸入器和血糖仪拆解操作的图像分割和关键点跟踪。数据集和源代码都可以公开获取。