基于SAM和pix2pix的商品数据集生成网络  

Product data set generation network based on SAM and pix2pix

作　　者：于惠钧[1,2] 邹志豪 康帅 Yu Huijun;Zou Zhihao;Kang Shuai(College of Railway Transportation,Hunan University of Technology,Zhuzhou 412007,China;College of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou 412007,China)

机构地区：[1]湖南工业大学轨道交通学院,湖南株洲412007 [2]湖南工业大学电气与信息工程学院,湖南株洲412007

出　　处：《电子技术应用》2025年第4期23-28,共6页Application of Electronic Technique

基　　金：国家重点研发计划(2022YFE0105200)。

摘　　要：针对商品包装快速变换带来的商品数据集采集和标注过程繁琐的问题,设计了一种基于SAM和pix2pix的商品数据集生成网络。该网络以单个商品多角度图像作为输入,生成与实际结算场景相近似的数据集。在RPC大型商品数据集上进行数据集生成,在YOLOv7、Fast R-CNN、AlexNet三种目标检测网络上验证生成数据集对目标检测效果的提升。实验结果表明,生成数据集融合到原数据集后用于训练模型能够有效提升商品识别准确率,并且与真实数据集相比具有较好的替代性。相较于原数据集,融合生成数据集三个网络上识别精度分别提升7.3%、4.9%、7.8%。通过该方法,显著提高了模型训练的效率与实用性,减轻传统商品数据集采集与标注所需的人力物力投入。Aiming at the cumbersome process of collection and labeling of commodity data set caused by rapid change of commodity packaging,this paper designs a commodity data set generation network based on Segment Anything Model(SAM)and Pixel to Pixel(pix2pix).The network uses multi-angle images of a single commodity as input to generate a data set similar to the actual settlement scene.The data set generation test was carried out on Retail Product Checkout Dataset(RPC)set,and the improvement of the generated data set on target detection effect was further verified on YOLOv7,Fast R-CNN and AlexNet target detection networks.The experimental results show that the generated data set can effectively improve the accuracy of commodity recognition,and has better substitution compared with the actual data set.Compared with the original data set,the recognition accuracy of the three networks generated by fusion data set is improved by 7.3%,4.9%and 7.8%,respectively.Through this method,the efficiency and practicability of model training are significantly improved,and the manpower and material input required for traditional commodity data collection and labeling is reduced.

关 键 词：商品识别 SAM pix2pix 数据集生成 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.4[自动化与计算机技术—控制科学与工程]