中文

Journal of Shanghai University >

2022 , Vol. 28 >Issue 3: 534 - 544

DOI: https://doi.org/10.12066/j.issn.1007-2861.2373

Microstructure Image Recognition and Microstructure Analysis

Anti-counterfeit label detection algorithm based on lightweight network

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  • 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 date: 2022-01-22

  Online published: 2022-05-27

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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

Cite this article

ZHANG Hongkun, HAN Yuexing, CHEN Qiaochuan, Wu Jinbo . Anti-counterfeit label detection algorithm based on lightweight network[J]. Journal of Shanghai University, 2022 , 28(3) : 534 -544 . DOI: 10.12066/j.issn.1007-2861.2373

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