Design of an Improved Dish Recognition Algorithm Based on Image Retrieval

With the booming of the new retail industry, unmanned billing systems have become the key to improve the operational efficiency of restaurants and reduce labour costs. In the field of payment and billing, traditional recognition systems based on deep neural networks often rely on a single network to achieve the detection and classification of Chinese dishes, but in the face of the increase or decrease in the number of dishes, it is necessary to frequently adjust the type of the network output and re-collect a large amount of image data for training, which leads to high update costs. In this study, a deep learning EfficientNet convolutional neural network combined with image retrieval for dish recognition is used to solve this problem. Firstly, EfficientNet, an advanced dish detection network, is used to accurately locate the positions of multiple dishes in an image, determine their orientations, and effectively separate the dishes from the background. Subsequently, the EfficientNet based feature extraction network calculates the image features of the separated dishes and uses cosine similarity as a metric to efficiently retrieve the dishes in the reference dish library, thus obtaining the classification results of the dishes. Finally, the system outputs the precise location and category information of the dishes. Experimental results show that the EfficientNet and cosine similarity approach achieves an average accuracy of 98.3% on the dish image dataset of China Food Network, which not only significantly shortens the updating time compared with traditional algorithms, but also requires only 1/24 of the amount of image data of the traditional deep learning classification methods. This breakthrough effectively solves the problem of the difficulty and high cost of updating the current dish recognition algorithms. This breakthrough effectively solves the current problem of difficult and costly updating of dish recognition algorithms, and is expected to significantly reduce the cost of updating algorithms, promote their wide application and promotion in the ‘new retail’ catering industry, and better meet the needs of the industry.