烟草科技2017，Vol.50Issue(9)：68-75,8.
基于卷积神经网络的烟丝物质组成识别方法
Identification of tobacco components in cut filler based on convolutional neural network
摘要
Abstract
For evaluating the blending consistence in cigarette production, the make-up of cut filler, including cut strips, cut stems, expanded cut strips and cut reconstituted tobacco, was identified with a model based on convolutional neural network incorporating deep learning approach. The local images, which reflected the microstructural characteristics of cut filler were used as the inputs of neural network. The output corresponding to each local characteristic image was analyzed and identified, and via statistical analysis the constituents of cut filler were determined. The results showed that the identification accuracies of the model for training samples and testing samples were 100% and 84.95%, respectively. The convolutional neural network combining with the corresponding method of result expressing in the model effectively promotes the identification accuracy for cut filler samples.关键词
卷积神经网络/叶丝/梗丝/膨胀叶丝/再造烟叶丝/反向传播/深度学习/结构特征/组成成分识别Key words
Convolutional neural network/Cut strip/Cut stem/Expanded cut strip/Cut reconstituted tobacco/Back propagation/Deep learning/Structural characteristic/Component identification分类
信息技术与安全科学引用本文复制引用
高震宇,王安,董浩,刘勇,王锦平,周明珠,夏营威,张龙..基于卷积神经网络的烟丝物质组成识别方法[J].烟草科技,2017,50(9):68-75,8.基金项目
国家烟草质量监督检验中心科技创新项目"基于计算机视觉的烟丝组分识别方法可行性研究"(522014CA0090) （522014CA0090）
安徽省重大科学仪器专项"食品和食品级接触材料中亚硝胺检测仪的研制及产业化"(151015223). （151015223）
