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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.06821 |
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| _version_ | 1866917902911799296 |
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| author | Zocco, Federico Lake, Daniel R. McLoone, Seán Rahimifard, Shahin |
| author_facet | Zocco, Federico Lake, Daniel R. McLoone, Seán Rahimifard, Shahin |
| contents | The circular economy paradigm is gaining interest as a solution to reducing both material supply uncertainties and waste generation. One of the main challenges in realizing this paradigm is monitoring materials, since in general, something that is not measured cannot be effectively managed. In this paper, we propose a real-time synchronized object detection framework that enables, at the same time, autonomous sorting, mapping, and quantification of solid materials. We begin by introducing the general framework for real-time wide-area material monitoring, and then, we illustrate it using a numerical example. Finally, we develop a first prototype whose working principle is underpinned by the proposed framework. The prototype detects 4 materials from 5 different models of inhalers and, through a synchronization mechanism, it combines the detection outputs of 2 vision units running at 12-22 frames per second (Fig. 1). This led us to introduce the notion of synchromaterial and to conceive a robotic waste sorter as a node compartment of a material network. Dataset, code, and demo videos are publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare Zocco, Federico Lake, Daniel R. McLoone, Seán Rahimifard, Shahin Computer Vision and Pattern Recognition The circular economy paradigm is gaining interest as a solution to reducing both material supply uncertainties and waste generation. One of the main challenges in realizing this paradigm is monitoring materials, since in general, something that is not measured cannot be effectively managed. In this paper, we propose a real-time synchronized object detection framework that enables, at the same time, autonomous sorting, mapping, and quantification of solid materials. We begin by introducing the general framework for real-time wide-area material monitoring, and then, we illustrate it using a numerical example. Finally, we develop a first prototype whose working principle is underpinned by the proposed framework. The prototype detects 4 materials from 5 different models of inhalers and, through a synchronization mechanism, it combines the detection outputs of 2 vision units running at 12-22 frames per second (Fig. 1). This led us to introduce the notion of synchromaterial and to conceive a robotic waste sorter as a node compartment of a material network. Dataset, code, and demo videos are publicly available. |
| title | Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.06821 |