Food State Recognition Using Deep Learning
Saeed Alahmari, Tawfiq Salem
Abstract
Automated food detection and recognition methods have been studied to enhance an end-user life. However, most of the 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 for simultaneously recognizing the state and type of food ingredients. We trained and evaluated the proposed approach on a benchmark dataset of food ingredients images with 9 different food states and 18 food types. We compared the proposed approach with a non-cascaded deep learning approach. The cascaded deep learning approach shows improvement in food ingredient state recognition with an accuracy of 87% compared to 81% using a non-cascaded deep learning method. Our proposed method broadly applies to various tasks where food ingredient state recognition is important, such as feeding elderly and disabled person, and for automation of food recognition and preparation.