Fast auto-clean CNN model for online prediction of food materials

Abstract Online food image detection is a key issue for intelligent food materials receiving and food supply chain applications, how to efficiently, accurately and quickly detect the image of food materials is a challenging research topic. A fast auto-clean convolutional neural network (CNN) model for online prediction of food materials is proposed, which is aiming at problems of the complex characteristics of the food images such as the complexity of the food materials, the focus of the dislocation and the uniformity of illumination. Firstly, a new approach of the auto-clean CNN models is proposed for automatic image cleaning and classification, which starting from original images and ending with multi-class prediction of clean images. Given a vocabulary of K classes, and a Yes/No clean label, two CNN models will learn a class label and a clean label respectively. Secondly, after the forward pass of two CNN models, the joint features generated from the last convolutional layers will be fed into our two loss layers. Combined with multi-class classification method, it classifies and optimizes the image dataset intelligently. Finally, an online prediction algorithm is proposed to improve the image recognition efficiency. Experimental results show that the proposed model and algorithm have good efficiency and accuracy, and the results of this study have significance to optimize the efficiency of the food supply chain industry and food quality evaluation.