Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants

TL;DR

This paper introduces Semantic Food Detection, a CNN-based method for automatic food tray analysis in restaurants, improving food detection accuracy and enabling faster, automated billing processes.

Contribution

It presents a novel integrated CNN framework for food localization, recognition, and segmentation, advancing state-of-the-art accuracy on a public dataset.

Findings

Achieved about 90% F-measure on UNIMIB2016 dataset

Significantly outperforms previous food detection methods

Facilitates automatic billing in restaurant environments

Abstract

The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.