论文标题
Myfood：一种食品细分和分类系统，以帮助营养监测
MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring
论文作者
论文摘要
没有食物监测的缺乏对人口体重的增加极大地贡献。由于缺乏时间和繁忙的例程，大多数人无法控制和记录饮食中消耗的东西。在计算机视觉中提出了一些解决方案以识别食物图像，但很少有人专门从事营养监测。这项工作介绍了智能系统的开发，该系统对图像中呈现的食物进行了分类和细分食品，以帮助自动监测用户饮食和营养摄入量。这项工作显示了针对食品识别的最先进方法的比较研究。在我们的方法论中，我们比较了FCN，ENET，SEGNET，DEEPLABV3+和MASK RCNN算法。我们构建了一个数据集，该数据集由最消耗的巴西食品类型组成，其中包含9种班级和1250张图像。使用以下指标评估了模型：汇总，敏感性，特异性，平衡精度和正预定义值。我们还提出了一个集成到移动应用程序中的系统，该系统自动识别和估计一餐中的营养，从而帮助人们进行更好的营养监测。提出的解决方案比市场上现有的解决方案更好。该数据集可在以下链接http://doi.org/10.5281/zenodo.4041488上公开获取
The absence of food monitoring has contributed significantly to the increase in the population's weight. Due to the lack of time and busy routines, most people do not control and record what is consumed in their diet. Some solutions have been proposed in computer vision to recognize food images, but few are specialized in nutritional monitoring. This work presents the development of an intelligent system that classifies and segments food presented in images to help the automatic monitoring of user diet and nutritional intake. This work shows a comparative study of state-of-the-art methods for image classification and segmentation, applied to food recognition. In our methodology, we compare the FCN, ENet, SegNet, DeepLabV3+, and Mask RCNN algorithms. We build a dataset composed of the most consumed Brazilian food types, containing nine classes and a total of 1250 images. The models were evaluated using the following metrics: Intersection over Union, Sensitivity, Specificity, Balanced Precision, and Positive Predefined Value. We also propose an system integrated into a mobile application that automatically recognizes and estimates the nutrients in a meal, assisting people with better nutritional monitoring. The proposed solution showed better results than the existing ones in the market. The dataset is publicly available at the following link http://doi.org/10.5281/zenodo.4041488
