DRNet: Towards fast, accurate and practical dish recognition

Existing algorithms of dish recognition mainly focus on accuracy with predefined classes, thus limiting their application scope. In this paper, we propose a practical two-stage dish recognition framework (DRNet) that yields a tradeoff between speed and accuracy while adapting to the variation in class numbers. In the first stage, we build an arbitrary-oriented dish detector (AODD) to localize dish position, which can effectively alleviate the impact of background noise and pose variations. In the second stage, we propose a dish reidentifier (DReID) to recognize the registered dishes to handle uncertain categories. To further improve the accuracy of DRNet, we design an attribute recognition (AR) module to predict the attributes of dishes. The attributes are used as auxiliary information to enhance the discriminative ability of DRNet. Moreover, pruning and quantization are processed on our model to be deployed in embedded environments. Finally, to facilitate the study of dish recognition, a well-annotated dataset is established. Our AODD, DReID, AR, and DRNet run at about 14, 25, 16, and 5 fps on the hardware RKNN 3399 pro, respectively.