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Research on Automatic Dish Recognition Algorithm Based on Deep Learning

机译：基于深度学习的自动洗碗识别算法研究

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Deep learning can automatically extract and learn multi-layer feature representations hidden between data, and has been successfully applied in image recognition and segmentation, semantic analysis and other fields. In order to improve restaurant settlement efficiency, save time and cost, this paper proposes a method that uses Convolution Neural Network and Huff transform to joint recognition, conducts preliminary detection of images taken with multiple dishes, uses Huff transform to extract and label individual dishes according to the shape of the plate. Then, the article constructs a convolutional neural network in the classification algorithm. Under different network parameters, this paper experiments on 10 dishes images in the cleaned VireoFood172 dataset. Through continuous optimization of network parameters, the classification accuracy of dishes Top2 has reached 87.2%. Under the fixed environment, the plate can be effectively extracted. It is proved that the method has a certain improvement in performance compared with the traditional dish recognition method, and can provide effective help for dish recognition.
机译：深度学习可以自动提取和学习隐藏在数据之间的多层特征表示，并且已成功应用于图像识别和分段，语义分析和其他字段。为了提高餐馆结算效率，节省时间和成本，本文提出了一种使用卷积神经网络和沟槽变换与联合识别的方法，进行多种菜肴拍摄的图像的初步检测，使用Huff变换提取和标记单个菜肴到板的形状。然后，该物品在分类算法中构造卷积神经网络。在不同的网络参数下，本文在清洁的Vireofood172数据集中的10个菜像上的实验。通过连续优化网络参数，DISI TOP2的分类精度已达到87.2％。在固定环境下，可以有效地提取板。事实证明，该方法与传统的盘识别方法相比，性能有一定的性能，可以提供有效的识别帮助。

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