A Tobacco Brand Recognition Method Based on HOG and SVM

Authors

  • Shujie Liu

DOI:

https://doi.org/10.62051/ijcsit.v4n1.18

Keywords:

Deep learning, Hog, Svm

Abstract

The appearance inspection system cannot automatically switch the brand, and a person needs to log into the system and manually switch the inspection brand in the process of production and brand change. To address this problem, a deep learning-based automatic cigarette brand recognition method is developed and validated. Experiments show that: the system can automatically detect the production brand, effectively save the time to change the brand, and has important reference significance for the application in the field of brand identification and automatic switching detection of the brand.

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References

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[4] HU Mingying, TAO Sheng. A image scaling method of PCA saliency detection combined with correlation analysis [J]. Journal of Jimei University (Natural Science Edition), 2022, 27(4):379-384.

[5] CUI Weiqing, DANG Changchun, ZHANG Wang, et al. A HOG feature template matching algorithm [J]. Mechanical Management and Development, 2018, 33(11):252-253.

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Published

13-09-2024

Issue

Section

Articles

How to Cite

Liu, S. (2024). A Tobacco Brand Recognition Method Based on HOG and SVM. International Journal of Computer Science and Information Technology, 4(1), 145-149. https://doi.org/10.62051/ijcsit.v4n1.18