基于图像识别的烟丝结构检测及烟丝组分分析

魏甲欣; 李琪; 马飞; 丁美宙; 靳亚伟; 王艺斌; 许文武; 王小明

doi:10.12187/2022.03.011

June 2022

Article Contents

WEI Jiaxin, LI Qi, MA Fei, et al. Cut tobacco structure detection and cut tobacco component analysis based on image recognition[J]. Journal of Light Industry, 2022, 37(3): 82-87. doi: 10.12187/2022.03.011

Citation: WEI Jiaxin, LI Qi, MA Fei, et al. Cut tobacco structure detection and cut tobacco component analysis based on image recognition[J]. Journal of Light Industry, 2022, 37(3): 82-87. doi: 10.12187/2022.03.011 shu

Cut tobacco structure detection and cut tobacco component analysis based on image recognition

WEI Jiaxin ¹ ,
LI Qi ¹ ,
MA Fei ¹ ,
DING Meizhou ² ,
JIN Yawei ¹ ,
WANG Yibin ³ ,
XU Wenwu ³ ,
WANG Xiaoming ¹

1. Xuchang Cigarette Factory, China Tobacco He'nan Industrial Co., Ltd., Xuchang 461000, China;
2. Technology Center, China Tobacco He'nan Industrial Co., Ltd., Zhengzhou 450000, China;
3. Nanjing Joint Technology Co., Ltd., Nanjing 210000, China

Received Date: 2021-09-18

Abstract

In order to improve the accuracy of cut tobacco structure detection and cut tobacco component analysis, based on image recognition technology to thinning the contour of cut tobacco image, extract the skeleton of cut tobacco, get the length of cut tobacco, and establish a fitting model for the apparent total area and mass of cut tobacco and get cut tobacco structure (whole cut rate and broken cut rate). The method of the smallest inscribed circle was used to obtain the average width and width variance of the cut tobacco, the color variance of the cut tobacco profile on the Saturation (S) channel, and the color moment of the HSV color model, the Support Vector Machine (SVM) was used as the classifier to construct the tobacco component classification model of tobacco flakes, cut stem, and cut tobacco. The practical application results showed that the method based on image recognition could accurately count whole cut rate and broken cut rate and was faster and more effective than the quality-control shake method. Compared with the convolutional neural network method and the residual neural network method, the average relative error of the method for identifying tobacco flakes, cut stem, and cut tobacco was less than 5%, and the accuracy and feasibility were higher.
- tobacco structure,
- tobacco component,
- image recognition,
- SVM,
- HSV
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Cut tobacco structure detection and cut tobacco component analysis based on image recognition

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