Multi-Spectral Segmentation with Synthesized Data for Refuse Sorting

Harald Ganster, Alfred Rinnhofer, Georg Waltner, Christian Payer, Heimo Gursch, Christian Oberwinkler, Reinhard Meisenbichler, Horst Bischof

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the OAGM Workshop 2021
EditorsMarkus Seidl, Matthias Zeppelzauer, Peter M. Roth
PublisherVerlag der Technischen Universität Graz
Number of pages3
Publication statusPublished - 2021
Event44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains: ÖAGM 2021 - University of Applied Sciences St. Pölten, abgesagt, Austria
Duration: 24 Nov 202125 Nov 2021


Conference44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains


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