缺陷定位与图像预处理:烟草自动化生产中的深度学习

J. Sensors Pub Date : 2022-08-17 DOI:10.1155/2022/6797207
Wei Wang, Lianlian Zhang, J. Wang, Zaiyun Long
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引用次数: 1

摘要

深度学习是近年来发展起来的一门新兴学科,旨在研究如何主动地从数据样本中获取多个特征表示,依靠数据驱动的方法,并应用一系列非线性变换来获得可靠的研究结果。结合当今的发展动态,传统的卷烟生产方式已经不能适应当前的经济发展速度。因此,卷烟企业必须通过生产经营的自动化和自动化管理技术来实现自身的快速稳定发展。本文在深度学习与烟草自动化生产研究的背景下,以深度学习相关的管理理论和深度卷积神经网络的研究方法为基础,重点研究了在烟草自动化生产中的应用,主要分析了分布式控制系统、生产指挥系统、物流系统、质量控制系统在烟草自动化系统中的应用。并得出自动化生产系统在烟草生产中起到了加强管理指挥、规避质量问题、节约成本等结论,希望能有一定的参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Locating Defects and Image Preprocessing: Deep Learning in Automated Tobacco Production
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.
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