VGG-16 Convolutional Neural Network-Oriented Detection of Filling Flow Status of Viscous Food

Abstract

A method is proposed to detect the filling flow status for automatic filling of thick liquid food. The method is based on a convolutional neural network algorithm and it solves the problem of poor accuracy in traditional flow detection devices. An adaptive threshold segmentation algorithm was first used to extract the region of interest for the acquired level image. Next, normalization and augmentation treatment were performed on the extracted images to construct a flow status dataset. A VGG-16 network trained on an ImageNet dataset was then used for isomorphic data-oriented feature migration and parameter tuning to automatically extract features and train the model. The identification accuracy and error rate of the network were verified and the advantages and disadvantages of the proposed method were compared to those of other methods. The experimental results demonstrated that the algorithm effectively detects multi-category flow status information and complies with the requirements for actual production.