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基于深度学习的食物卡路里估算方法
引用本文:梁炎超,李建华.基于深度学习的食物卡路里估算方法[J].医学教育探索,2018,44(2):270-276.
作者姓名:梁炎超  李建华
作者单位:华东理工大学信息科学与工程学院, 上海 200237,华东理工大学信息科学与工程学院, 上海 200237
基金项目:国家自然科学基金(61402174)
摘    要:现代智能移动设备可利用计算机视觉技术从食物照片中估算卡路里。针对现有食物卡路里估算方法识别准确率不高、估算结果误差较大的问题,本文提出了一种基于深度学习的食物卡路里估算方法。该方法需以食物俯视图和侧视图作为输入,先进行目标检测,再用GrabCut算法获得目标轮廓。通过对不同类别的食物采用不同的体积估算方法,以提高体积估算准确性。实验结果表明,本文方法估算结果较为准确,能为用户控制卡路里摄入提供正确的参考。

关 键 词:深度学习  体积估算  卡路里
收稿时间:2017/3/17 0:00:00

Food Calorie Estimation Method Based on Deep Learning
LIANG Yan-chao and LI Jian-hua.Food Calorie Estimation Method Based on Deep Learning[J].Researches in Medical Education,2018,44(2):270-276.
Authors:LIANG Yan-chao and LI Jian-hua
Institution:School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China and School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
Abstract:Obesity is associated with the increased risk of diseases. For obesity treatment, it is necessary to record all food intakes per day. However, in most cases, patients do have troubles in estimating the amount of food intake because they are unwillingness to record or lack of related nutritional information. The calorie in food can be estimated via computer vision methods, whose estimated accuracy is determined by two main factors:object detection algorithm and volume estimation method. In order to increase the accuracy of detection and reduce the error of volume estimation in food calorie estimation, this paper proposes a calorie estimation method based on deep learning. This proposed method takes two food images as its inputs:a top view and a side view. Each image includes a calibration object that is used to estimate image''s scale factor. Food(s) and calibration object are detected by object detection method called faster region-based convolutional neural networks (Faster R-CNN) and each food''s contour is obtained by applying GrabCut algorithm. The calibration object judged by Faster R-CNN is used to calculate the scale factor of each view. Each food''s volume can be estimated according to its contour in top view, contour in side view, and scale factors. For improving the volume estimation accuracy, this paper divides different types of food shape into four types, for which the corresponding volume estimation formula is adopted. And then, each food''s mass and calorie are estimated by means of density table and nutrition table. In the proposed volume estimation experiments, the error between a estimation result and its corresponding true value does not exceed ±20% for most food. The experimental results show that the estimation results are accurate. Hence, the proposed method in this paper is helpful for those patients who want to control calorie intake. In future research, we will keep on improving our method and develop mobile application.
Keywords:deep learning  volume estimation  calorie
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