A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users’ subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints. It will be followed by a discussion on deep learning method and its efficacy in dietary assessment. After a comprehensive exploration, we found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment.

中文翻译：

---

用于饮食评估的基于图像的食物分类和体积估计：综述。

每日饮食评估方法称为 24小时饮食回想在营养流行病学研究中通常使用来捕获参与者食用的食物的详细信息，以帮助了解他们的饮食行为。然而，在这种自我报告技术中，所报告的食物类型和份量在很大程度上取决于使用者的主观判断，这可能会导致饮食分析结果有偏差和不准确。结果，最近已经提出了多种基于视觉的饮食评估方法。尽管这些方法显示了解决营养流行病学研究中的问题的希望，但本研究中详述的一些挑战和即将来临的机遇仍然存在。这项研究概述了基于图像的饮食评估领域中使用的计算算法，数学模型和方法。它还就处理速度，模型准确性，效率和约束方面，对食品识别和体积/重量估算中的最新方法进行了全面比较。接下来将讨论深度学习方法及其在饮食评估中的功效。经过全面的探索，我们发现结合不同方法的综合膳食评估系统可能是解决准确膳食摄入评估难题的潜在解决方案。