A Textured Feature Enhanced Efficient Network to Classify Tobacco Strip Structure Types

Tobacco strip structure is the size and sheet structure of tobacco leaves in the process of leaf beating, which is the key economic indicator of cigarette manufacturing. In this article, we turn the problem of getting the tobacco strip structure into a classification problem by dividing our photographed images in the complex industrial scenarios into 14 types of tobacco strip structures according to expert knowledge and then using deep learning to recognize them. We use Efficientnetv2 as the backbone and develop it based on the characteristics of our dataset. As for the proposed method, the overall image classification accuracy reaches 92.82%, which is 2.33% higher than the accuracy before our improvements. 14 types of tobacco strip structures after leaf beating can be identified quickly and efficiently by using our present model. The results of this study can provide a theoretical basis and technical support for the intelligent identification of tobacco strip structure after leaf beating.