Abstract
Refuse sorting is a key technology to increase the recycling rate and reduce the growths of landfills worldwide. However, monitoring and parameterization of sorting facilities is still done in a mostly static fashion. This work combines multi-spectral imaging with deep learning based image recognition to monitor and dynamically optimize processes in sorting facilities.
Our solution is capable of monitoring the sorting process remotely avoiding potentially harmful working conditions due to dust, bacteria, and fungal spores. Furthermore, the introduction of objective sorting performance measures enables informed decisions to improve the sorting parameters and react quicker to changes in the refuse composition.
Our solution is capable of monitoring the sorting process remotely avoiding potentially harmful working conditions due to dust, bacteria, and fungal spores. Furthermore, the introduction of objective sorting performance measures enables informed decisions to improve the sorting parameters and react quicker to changes in the refuse composition.
Original language | English |
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Title of host publication | Proceedings of the OAGM Workshop 2021 |
Editors | Markus Seidl, Matthias Zeppelzauer, Peter M. Roth |
Publisher | Verlag der Technischen Universität Graz |
Number of pages | 3 |
DOIs | |
Publication status | Published - 2021 |
Event | 44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains: ÖAGM 2021 - University of Applied Sciences St. Pölten, abgesagt, Austria Duration: 24 Nov 2021 → 25 Nov 2021 |
Conference
Conference | 44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains |
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Country/Territory | Austria |
City | abgesagt |
Period | 24/11/21 → 25/11/21 |