Multi-source data fusion using deep learning for smart refrigerators

Food recognition is one of the core functions for a smart refrigerator. But there are many challenges for accurate food recognition due to reasons of too many kinds of food inside the refrigerator which tends to obscure each other, and they may look very similar. This paper proposes a fruit recognition approach that fuses weight information and multi deep learning models. The proposed approach can remarkably improve recognition accuracy. We have extensively evaluated the proposed approach for its performance and accuracy, which demonstrate the effectiveness of the proposed approach.