Locating Defects and Image Preprocessing: Deep Learning in Automated Tobacco Production

Abstract

Deep learning is an emerging discipline developed in recent years, which is aimed at investigating how to actively obtain multiple feature representations from data samples, rely on data-driven methods, and apply a series of nonlinear transformations to obtain reliable research results. Combined with today’s development dynamics, the traditional way of cigarette production can no longer adapt to the current rate of economic development. Therefore, cigarette companies must achieve their own rapid and stable development through automation and automated management techniques for production and operation. In this paper, in the context of the research on deep learning and tobacco automation production, we focus on the application in tobacco automation production based on the management theory related to deep learning and the research method of deep convolutional neural network, mainly analyzing the application of distributed control system, production command system, logistics system, and quality control system in tobacco automation system, and conclude that the automated production system plays a role in tobacco production strengthen management and command, circumvent quality problems, save costs, and other conclusions, which hopefully have some reference.