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arXiv 2002.037862020-02-06cs.CVcs.LGeess.IV

Deep Learning for Classifying Food Waste

Institute of Embedded Systems Zurich University of Applied Sciences（苏黎世应用科技大学嵌入式系统研究所）；Zurich University of Applied Sciences（苏黎世应用科技大学）

Amin Mazloumian, Matthias Rosenthal, Hans Gelke

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One third of food produced in the world for human consumption -- approximately 1.3 billion tons -- is lost or wasted every year. By classifying food waste of individual consumers and raising awareness of the measures, avoidable food waste can be significantly reduced. In this research, we use deep learning to classify food waste in half a million images captured by cameras installed on top of food waste bins. We specifically designed a deep neural network that classifies food waste for every time food waste is thrown in the waste bins. Our method presents how deep learning networks can be tailored to best learn from available training data.

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