AOS: Anti-Obesity System With Deep Learning-based Classification Model Using a Novel Data Augmentation Technique

Abstract

Obesity has been a worldwide leading cause of associated health risks for decades, yet not much is being done to solve this issue. Previously, minimal efforts have been made to address this issue in regards to mandating nutrition labels on the back of packaged foods. However, the mundane and growingly elusive packing labels have diminished the proper effects of this constitution as people rarely ever take the time nor effort to thoroughly read through the labels. Moreover, most dishes served from restaurants do not serve with any nutritional information, which places many of the people dining out in complete darkness of what they are truly consuming. To solve this problem, we propose the deep learning-based AOS (Anti-Obesity System), which analyzes images of common junk foods and unhealthy meals to classify the category of food. The proposed system consists of a deep learning-based classifier and a post-processing module that outputs relevant nutritional information regarding each category of food. In addition, we propose a novel data augmentation technique in order to make the trained model produce better results. We also conduct the ablation study to experimentally prove that the proposed method enforces the model to work better in real-world situations. We achieve an AP of 59.30% on the proposed food dataset. Code will be available at https://github.com/smkim0508/ObesityNutritionClassifier.git.