Classification of Tobacco Defects based on Vgg16

Shujie Liu

doi:10.54097/mmeyx924

Authors

Shujie Liu

DOI:

https://doi.org/10.54097/mmeyx924

Keywords:

Deep Learning; Vgg16; Defect Classification.

Abstract

In the existing cigarette packet defect detection using simple image processing methods, the defect detection capability is limited and the corresponding defects cannot be counted. This paper addresses this problem and proposes a vgg16-based defect classification method for cigarette packets, which can effectively detect and count the defects of cigarette packets. Experiments have proved that the detection accuracy can reach 100% under ideal conditions.

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