中国烟草学报2026，Vol.32Issue(3)：64-70,7.DOI:10.16472/j.chinatobacco.2025.T0343
基于生成对抗网络的烟包缺陷检测算法研究
Research on cigarette pack defect detection algorithm based on generative adversarial network
许景 1阿银椿 1朱峰 2段青娜 1唐书语 1周家超 1赵朝琨 1金思航 1马子瑞1
作者信息
- 1. 红云红河烟草(集团)有限责任公司昆明卷烟厂,云南省昆明市五华区红锦路 366号 650231
- 2. 云南中烟培训中心(鉴定站),云南省昆明市盘龙区盘井街 345号 650000
- 折叠
摘要
Abstract
[Purpose]This paper aims to propose an efficient and accurate method for detecting cosmetic defects in cigarette packs based on the practical needs of the cigarette industry.[Methods]A two-layer Generative Adversarial Network(GAN)-based approach for cigarette pack defect detection is proposed.This method employs a two-layer architecture requiring only positive samples for training:the first layer uses grayscale histogram similarity comparison to rapidly eliminate easily detectable defective samples,reducing computational load;The second layer introduces GAN with spatial attention mechanisms,achieving refined defect identification by comparing generative loss between normal images and defective samples.Comparative experiments were conducted against One-Class Support Vector Machines(One-Class SVM)and Variational Autoencoders(VAE).[Results]Experiments demonstrated that the proposed method exhibits excellent performance in both true positive rate and false positive rate,achieving the optimal overall AUC value of 98.41%on the cigarette packaging defect dataset.关键词
生成对抗网络/烟包外观检测/深度学习/卷积神经网络/图像处理Key words
generative adversarial networks/appearance inspection of cigarette packs/deep learning/convolutional neural network/image processing引用本文复制引用
许景,阿银椿,朱峰,段青娜,唐书语,周家超,赵朝琨,金思航,马子瑞..基于生成对抗网络的烟包缺陷检测算法研究[J].中国烟草学报,2026,32(3):64-70,7.
