计算机工程与应用2025，Vol.61Issue(11)：306-315,10.DOI:10.3778/j.issn.1002-8331.2403-0106
基于Transformer的生成对抗网络的柔印标签在线检测
Online Detection of Flexographic Printing Labels Based on Transformer-Based Generative Adversarial Network
摘要
Abstract
To control the production quality of flexographic labels in a timely manner,this paper integrates Transformer architecture to design a generative adversarial network and proposes an online detection method for flexographic labels.To address the issue of rare defective samples and uneven sample distribution in actual production,the paper introduces a novel noise addition scheme to simulate defective samples,training the template generator solely with qualified flexo-graphic label samples.A generative adversarial network is designed by combining skip connections and Transformer blocks,along with corresponding loss functions,to enhance the generator's capability to represent templates.Lastly,a defect evaluation scheme based on adaptive thresholds is devised for the detection of flexographic labels.Experimental results indicate that the detection method proposed in this paper achieves a detection performance with an average false positive rate of 2.23%,an average false negative rate of 0%,and an F1-score of 0.983 within a reasonable detection time of 38 ms per sample,outperforming existing anomaly detection deep learning methods on this dataset.关键词
图像处理/缺陷检测/生成对抗网络/柔印标签/随机噪声Key words
image processing/defect detection/generative adversarial network/flexographic printing labels/random noise分类
信息技术与安全科学引用本文复制引用
龙进良,蓝学深,蔡念,燕舒乐,肖盼,许少秋,周映红..基于Transformer的生成对抗网络的柔印标签在线检测[J].计算机工程与应用,2025,61(11):306-315,10.基金项目
国家自然科学基金(62171142) （62171142）
广东省科技计划项目(2021A1515011908) （2021A1515011908）
茂名市科技项目(2022S048). （2022S048）
