基于计算机视觉与机器学习的烟丝杂质图像级联检测方法

付永民; 范磊; 李长进; 吴庆华

doi:10.12187/2023.04.015

August 2023

Article Contents

FU Yongmin, FAN Lei, LI Changjin and et al. Research on cascade detection technology of tobacco impurities images based on computer vision and machine learning[J]. Journal of Light Industry, 2023, 38(4): 113-121. doi: 10.12187/2023.04.015

Citation: FU Yongmin, FAN Lei, LI Changjin and et al. Research on cascade detection technology of tobacco impurities images based on computer vision and machine learning[J]. Journal of Light Industry, 2023, 38(4): 113-121. doi: 10.12187/2023.04.015 shu

Research on cascade detection technology of tobacco impurities images based on computer vision and machine learning

Xuchang Cigarette Factory, Henan China Tobacco Industry Co., Ltd., Xuchang 461000, China

Received Date: 2022-01-01
Accepted Date: 2022-03-21

Abstract

To improve the accuracy of tobacco impurity detection and removal, a cascade detection method for tobacco impurity images based on computer vision and machine learning was designed. This method used color features and gradient energy computer vision methods to locate tobacco impurities. Combining HOG and LBP features with a cascade Adaboost classifier, a multi-window detection algorithm was designed to detect tobacco impurities in real time. The experimental results showed that the accuracy of the static impurity detection method based on color features was higher than that of the gradient energy method. After combining HOG features and multi-level cascade Adaboost classifier algorithm, the detection results were very stable, and the accuracy of tobacco impurity detection reached 97.33%. In the actual operation process, there was no need to manually adjust the algorithm parameters, ensuring the accuracy and effectiveness of the algorithm while reducing the time cost.
- tobacco impurities detection,
- computer vision,
- multi-window detection,
- cascaded Adaboost classifier,
- color feature
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沈阳化工大学材料科学与工程学院沈阳 110142

Research on cascade detection technology of tobacco impurities images based on computer vision and machine learning

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