Diet is central to the epidemic of lifestyle disorders. Accurate and effortless diet logging is one of the significant bottlenecks for effective diet management and calorie restriction. Dish detection from food platters is a challenging problem due to a visually complex food layout. We present an end-to-end computational framework for diet management, from data compilation, annotation, and state-of-the-art model identification to its mobile app implementation. As a case study, we implement the framework in the context of Indian food platters known for their complex presentation that poses a challenge for the automated detection of dishes. Starting with the 61 most popular Indian dishes, we identify the state-of-the-art model through a comparative analysis of deep-learning-based object detection architectures. Rooted in a meticulous compilation of 68,005 platter images with 134,814 manual dish annotations, we first compare ten architectures for multi-label classification to identify ResNet152 (mAP=84.51%) as the best model. YOLOv8x (mAP=87.70%) emerged as the best model architecture for dish detection among the eight deep-learning models implemented after a thorough performance evaluation. By comparing with the state-of-the-art model for the IndianFood10 dataset, we demonstrate the superior object detection performance of YOLOv8x for this subset and establish Resnet152 as the best architecture for multi-label classification. The models thus trained on richly annotated data can be extended to include dishes from across global cuisines. The proposed framework is demonstrated through a proof-of-concept mobile application with diverse applications for diet logging, food recommendation systems, nutritional interventions, and mitigation of lifestyle disorders.

翻译：饮食是导致生活方式疾病流行的核心因素。准确且便捷的饮食记录是有效饮食管理和热量限制的重要瓶颈之一。由于食物布局在视觉上的复杂性，从餐盘中检测菜品是一项具有挑战性的问题。我们提出了一种端到端的饮食管理计算框架，涵盖数据编译、标注、先进模型识别及其移动应用实现。作为案例研究，我们以以复杂摆盘著称的印度餐盘为背景实施该框架——其复杂摆盘对菜品自动检测构成了挑战。从61种最受欢迎的印度菜品出发，我们通过基于深度学习的物体检测架构的比较分析，识别出最先进的模型。基于对68,005张餐盘图像（包含134,814个手工菜品标注）的严谨编译，我们首先比较了十种用于多标签分类的架构，确定ResNet152（mAP=84.51%）为最优模型。在八种深度学习模型中，经过全面性能评估后，YOLOv8x（mAP=87.70%）成为菜品检测的最佳模型架构。通过与IndianFood10数据集上的现有最优模型对比，我们证明了YOLOv8x在该子集上的卓越物体检测性能，并确立了ResNet152作为多标签分类最佳架构。这些基于丰富标注数据训练的模型可扩展至涵盖全球各类菜系的菜品。通过一个概念验证型移动应用程序，本框架在饮食记录、食品推荐系统、营养干预及生活方式疾病缓解等多样化应用中得到了展示。