With improvements in human living standard, people’s demands on food quality are getting higher and higher. Effective food recognition algorithms are needed to obtain more useful food information. In order to solve the problem of low accuracy and slow speed of food recognition algorithms, a new food recognition algorithm based on CNN algorithm is proposed. First, the proposed algorithm preprocess the food images which are collected from the internet. And then use the traditional convolution extract the features from food images. The jumping convolution which is designed in this paper to extract food features jumping and combines the features from traditional convolutions. This algorithm cannot only solve the food recognition problem effectively, but also reduce the calculation parameters. Compared with the experimental results of other deep learning networks, the proposed algorithm has a good effect, and can recognize the food quickly and reduce the training time.

随着人们生活水平的提高，人们对食品质量的要求越来越高。需要有效的食物识别算法来获取更多有用的食物信息。为解决食品识别算法精度低，速度慢的问题，提出了一种基于CNN算法的食品识别新算法。首先，提出的算法对从互联网收集的食物图像进行预处理。然后使用传统的卷积从食物图像中提取特征。本文设计的跳跃卷积用于提取跳跃的食物特征，并结合了传统卷积的特征。该算法不仅有效地解决了食品识别问题，而且减少了计算参数。