FWA: Visual Food Waste Analysis for Sustainable Kitchens

At a glance

Project leader : Prof. Dr. Matthias Rosenthal
Co-project leader : Prof. Dr. Thilo Stadelmann
Project team : Mohammadreza Amirian, Philipp Andermatt, Dr. Ricardo Chavarriaga, Rico Ganahl, Philipp Huber, Dr. Amin Mazloumian, Dominic Mösch, Pascal Sager, Yvan Putra Satyawan, Dr. Frank-Peter Schilling, Raphael Zingg
Project budget : CHF 435'610
Project status : completed
Funding partner : Innosuisse (Innovationsprojekt / Projekt Nr. 36777.1 IP-ICT)
Project partner : Kitro SA
Contact person : Matthias Rosenthal

Description

A novel approach for a fully automated food waste management solution for commercial kitchens is investigated. Food waste is automatically detected using a new camera device, preprocessed in real-time and classified using machine learning algorithms.

Publications

Simmler, Niclas; Sager, Pascal; Andermatt, Philipp; Chavarriaga, Ricardo; Schilling, Frank-Peter; Rosenthal, Matthias; Stadelmann, Thilo,

2021.

A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications [paper].

In:

Proceedings of the 8th SDS.

8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021.

IEEE.

pp. 26-31.

Available from: https://doi.org/10.1109/SDS51136.2021.00012

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