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arXiv 2309.04763cs.CV
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面向循环医疗的视觉材料特性学习

Visual Material Characteristics Learning for Circular Healthcare

Loughborough University(拉夫堡大学)

Federico Zocco, Shahin Rahimifard

AI总结:

本文针对循环医疗开发了视觉系统,解决资源测绘、废物分拣和拆解任务,并发布两个相关数据集,证明表征学习视觉能改善回收链。

摘要:

由于废物污染和材料供应的不确定性,以线性获取-制造-处置模式为基础的传统经济已被证明是不可持续的。因此,提高材料流动的循环性是必要的。在本文中,我们通过开发针对三大循环经济任务的视觉系统向循环医疗迈进了一步:资源测绘与量化、废物分拣和拆解。我们系统的性能表明,表征学习视觉可以改善回收链,在回收链中,由于污染风险,自主系统是关键的推动者。我们还发布了两个完全标注的数据集,分别用于吸入器和血糖仪拆解操作的图像分割和关键点跟踪。数据集和源代码已公开。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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