目的 针对蔬菜托盘包装中传统防伪码易复制、纸基功能标签复杂显色背景下身份识别稳定性不足等问题,提升低成本纸基防伪标签的不可复制性及复杂背景下的识别可靠性。方法 基于颗粒材料喷射随机沉积原理研发了随机撒点装置,可在纤维素滤纸表面形成随机颗粒图案,制得了物理不可克隆函数（PUF）纸基随机防伪标签并开发了“YOLO11n目标检测—防伪区域裁剪—MobileNetV3-Small分类识别”的三阶段识别算法程序,提高模型对功能性显色纸基标签背景变化的适应性。结果 结果表明,PUF纸基防伪标签具有较好随机性和清晰的颗粒结构特征;YOLO11n检测模型的Precision、Recall和mAP50分别达到0.999、1.000和0.995,散点图案具有良好的可识别性。进一步通过多材料、多背景数据扩增与模型重新训练,提高了系统在功能性显色纸基标签不同颜色背景下的识别适应性。结论 本研究创制的随机颗粒PUF纸基防伪标签不仅具有低成本、易制备的优点,而且能够提高蔬菜托盘包装的防复制能力和复杂显色背景下的识别稳定性。该成果为品质指示与身份防伪一体化智能标签开发提供了参考,有助于提升农产品来源可信度和终端识别便利性。
Abstract
The work aims to improve the unclonability of low-cost paper-based anti-counterfeiting labels and their recognition reliability under complex backgrounds, so as to address the problems that conventional anti-counterfeiting codes used in vegetable tray packaging are easily replicated and that identity recognition of paper-based functional labels is unstable under complex color-changing backgrounds. Based on the principle of random deposition of sprayed particle materials, a random sprinkling device was developed to generate random particle patterns on the surface of cellulose filter paper, thereby fabricating physical unclonable function (PUF)-based paper random anti-counterfeiting labels. A three-stage recognition algorithm combining "YOLO11n-based target detection, anti-counterfeiting region cropping, and MobileNetV3-Small-based classification recognition" was further developed to improve the adaptability of the model to background changes in functional color-changing paper-based labels. The results showed that the PUF paper-based anti-counterfeiting labels exhibited good randomness and clear particle structural features. The Precision, Recall, and mAP50 of the YOLO11n detection model reached 0.999, 1.000, and 0.995, respectively, indicating good recognizability of the random particle patterns. Furthermore, multi-material and multi-background data augmentation combined with model retraining improved the recognition adaptability of the system under different color backgrounds of functional color-changing paper-based labels. The random-particle PUF paper-based anti-counterfeiting label developed in this study not only features low cost and ease of fabrication, but also improves the anti-replication capability of vegetable tray packaging and the recognition stability under complex color-changing backgrounds. This work provides a reference for the development of integrated smart labels combining quality indication and identity anti-counterfeiting functions, and can help enhance the source credibility of agricultural products and the convenience of terminal recognition.
关键词
纸基标签 /
物理不可克隆函数（PUF） /
随机防伪 /
蔬菜智能包装 /
图像识别
Key words
paper-based label /
physical unclonable function (PUF) /
random anti-counterfeiting /
smart packaging for vegetables /
image recognition
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参考文献
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基金
国家重点研发计划课题（2023YFD2001303）
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