[论文解读] Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning

Md. Shafaat Jamil Rokon, Md. Kishor Morol+3 more|arXiv (Cornell University)|Mar 13, 2022

Advanced Chemical Sensor Technologies被引用 4

一句话总结

本文提出了一种基于深度学习的食品食谱推荐框架，通过使用卷积神经网络（CNN）从图像中检测食材，并利用机器学习推荐食谱。该系统在包含9,856张图像、32种食材类别的自定义数据集上进行训练，食材识别准确率达到94%，可有效为新手和经验丰富的厨师提供食谱推荐。

ABSTRACT

Food is essential for human survival, and people always try to taste different types of delicious recipes. Frequently, people choose food ingredients without even knowing their names or pick up some food ingredients that are not obvious to them from a grocery store. Knowing which ingredients can be mixed to make a delicious food recipe is essential. Selecting the right recipe by choosing a list of ingredients is very difficult for a beginner cook. However, it can be a problem even for experts. One such example is recognising objects through image processing. Although this process is complex due to different food ingredients, traditional approaches will lead to an inaccuracy rate. These problems can be solved by machine learning and deep learning approaches. In this paper, we implemented a model for food ingredients recognition and designed an algorithm for recommending recipes based on recognised ingredients. We made a custom dataset consisting of 9856 images belonging to 32 different food ingredients classes. Convolution Neural Network (CNN) model was used to identify food ingredients, and for recipe recommendations, we have used machine learning. We achieved an accuracy of 94 percent, which is quite impressive.

研究动机与目标

• 解决用户拥有食材但缺乏其名称或搭配知识时，难以推荐合适食谱的挑战。
• 提高从图像中识别食品食材的准确性，克服传统图像处理方法的局限性。
• 开发一个端到端系统，利用机器学习将检测到的食材与相关且高质量的食谱相链接。
• 构建一个包含9,856张图像、覆盖32种食品食材类别的自定义数据集，用于模型的训练与评估。

提出的方法

• 收集并标注了一个包含9,856张图像的自定义数据集，涵盖32种不同的食品食材类别。
• 训练卷积神经网络（CNN）以高精度从输入图像中分类食品食材。
• 将食材检测结果用作基于机器学习的食谱推荐算法的输入特征。
• 使用标准分类指标对模型进行评估，以准确率为首要性能度量指标。
• 系统整合基于图像的食材识别与食谱检索，实现实时推荐。

实验结果

研究问题

• RQ1 基于深度学习的CNN模型能否准确检测来自真实世界图像中的多样化食品食材？
• RQ2 将食材检测与食谱推荐相结合，在提升家庭厨师用户体验方面有多高效？
• RQ3 包含32种食材类别的自定义数据集在多大程度上支持准确且可泛化的食材识别？
• RQ4 在真实烹饪场景中，结合图像识别与食谱推荐的端到端流程可实现多高的准确率？

主要发现

• 所提出的基于CNN的食材检测模型在包含9,856张图像、32种食品食材类别的自定义数据集上，测试准确率达到94%。
• 该系统成功将检测到的食材与相关食谱相链接，证明了其在家庭厨师中的实际应用价值。
• 将深度学习用于食材识别，结合机器学习用于食谱推荐，形成了一条稳健且准确的推荐流水线。
• 该包含9,856张图像的自定义数据集为未来食品食材识别研究提供了宝贵的基准。