基于轻量级网络的防伪标签检测算法

doi:10.12066/j.issn.1007-2861.2373

上海大学学报(自然科学版) ›› 2022, Vol. 28 ›› Issue (3): 534-544.doi: 10.12066/j.issn.1007-2861.2373

• 材料微结构图像识别与微结构分析 • 上一篇下一篇

基于轻量级网络的防伪标签检测算法

张宏坤¹, 韩越兴¹^,²(), 陈侨川¹, 巫金波²^,³

1.上海大学计算机工程与科学学院, 上海 200444
2.之江实验室, 浙江杭州 311100
3.上海大学材料基因组工程研究院, 上海 200444

收稿日期:2022-01-22 出版日期:2022-06-30 发布日期:2022-05-27
通讯作者: 韩越兴 E-mail:han_yx@i.shu.edu.cn
作者简介:韩越兴(1975—), 男, 副教授,博士生导师, 博士, 研究方向为图像处理、计算机视觉等. E-mail: han_yx@i.shu.edu.cn
基金资助:
国家重点研发计划资助项目(2018YFB0704400);国家重点研发计划资助项目(2020YFB0704503);上海市自然科学基金资助项目(20ZR1419000);之江实验室科研攻关资助项目(2021PE0AC02)

Anti-counterfeit label detection algorithm based on lightweight network

ZHANG Hongkun¹, HAN Yuexing¹^,²(), CHEN Qiaochuan¹, Wu Jinbo²^,³

1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2. Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
3. Materials Genome Institute of Shanghai University, Shanghai 200444, China

Received:2022-01-22 Online:2022-06-30 Published:2022-05-27
Contact: HAN Yuexing E-mail:han_yx@i.shu.edu.cn

摘要/Abstract

摘要：

近年来, 伪造盗版产品带来的经济损失逐年增大, 伪造技术不断提升, 防伪检测问题受到了广泛关注. 为了解决现有防伪检测方法的计算量大、资源占用高、检测耗时较长等问题, 提出了一种基于轻量级网络的防伪标签识别检测模型, 该模型采用更为轻量的卷积神经网络(convolutional neural network, CNN)来进行形状和纹理的识别. 在形状识别任务中, 降低池化层大小以增强模型学习能力; 在纹理分类任务中, 使用协调注意力(coordinate attention, CA)模块来增强模型对单一特征图的信息获取. 通过设计损失函数增强模型对真伪样本识别能力, 并通过特征向量最大值得到预测结果. 实验结果表明, 该方法整体识别检测的准确率可达 95.67%, 检测时间相较于传统方法有显著减少.

关键词: 防伪检测, 轻量级神经网络, 形状纹理识别

Abstract:

Economic loss caused by counterfeit and pirated products has increased annually; in this regard, counterfeiting technology has been improved continuously, where researchers attempt to improve anti-counterfeiting detection. To alleviate the complex computation, high resource requirement, and long detection time of existing anti-counterfeiting detection methods, an anti-counterfeiting label identification and detection model based on a lightweight network is proposed herein. Convolutional neural networks (CNNs) are adopted for shape and texture recognition. During shape recognition, the size of the pooling layer is reduced to enhance model learning ability. A coordinate attention (CA) module is used in texture classification to enhance the information acquisition of a single feature graph. The loss function is designed to enhance the model's ability to identify authentic samples. Finally, the prediction result is obtained using the maximum feature vector. Experimental results show that the maximum overall detection accuracy achievable by the proposed method is 95.67%, and that the detection time improved significantly as compared with the conventional method.

Key words: anti-counterfeiting detection, lightweight neural network, shape texture recognition

中图分类号:

TP 399

张宏坤, 韩越兴, 陈侨川, 巫金波. 基于轻量级网络的防伪标签检测算法[J]. 上海大学学报(自然科学版), 2022, 28(3): 534-544.

ZHANG Hongkun, HAN Yuexing, CHEN Qiaochuan, Wu Jinbo. Anti-counterfeit label detection algorithm based on lightweight network[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 534-544.

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基于轻量级网络的防伪标签检测算法

Anti-counterfeit label detection algorithm based on lightweight network

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