Food State Recognition Using Deep Learning

Abstract

Automated food detection and recognition methods have been studied to enhance end-user life. However, most existing research focused on food ingredient type recognition, with little work has been done for food ingredient state recognition. Successful recognition of food ingredient state plays a significant role in handling the food ingredient by an intelligent system. In this work, we propose a new novel cascaded multi-head approach based on deep learning to simultaneously recognize the state and type of food ingredients. We trained and evaluated the proposed approach on a benchmark dataset of food ingredient images with nine different food states and 18 food types. We compared the proposed approach with a non-cascaded deep learning approach. The cascaded approach shows improvement in food ingredient state recognition with 87% accuracy compared to 81% using a non-cascaded deep learning method. Our proposed method broadly applies to various tasks where food ingredient state recognition is essential, such as feeding elderly and disabled people and automating food recognition and preparation.