刘照邦,袁明辉.基于深度神经网络的货架商品识别方法[J].包装工程,2020,41(1):149-155.
LIU Zhao-bang,YUAN Ming-hui.Product Recognition on Shelves Based on Deep Neural Network[J].Packaging Engineering,2020,41(1):149-155. |
| 基于深度神经网络的货架商品识别方法 |
| Product Recognition on Shelves Based on Deep Neural Network |
| 投稿时间：2019-04-27 修订日期：2020-01-10 |
| DOI：10.19554/j.cnki.1001-3563.2020.01.023 |
| 中文关键词: 货架商品识别 深度神经网络 目标检测 图像分类 存货单位 |
| 英文关键词: product recognition on shelves deep neural network object detection classification Stock Keeping Unit |
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| 中文摘要: |
| 目的 为快速统计货架商品信息，提出一种基于深度神经网络的货架商品自动识别方法。方法 摄像头采集的货架商品图像经过深度神经网络算法处理，得到了图像中商品的SKU和位置。针对货架商品识别这种密集检测场景，文中方法改进了通用深度神经网络目标检测算法，将算法分为检测和分类2个阶段且重新设计了部分网络结构。最后，将文中方法和传统货架商品识别方法以及通用深度神经网络目标检测方法进行了比较。结果 实验证明该方法的检测阶段的模型平均正确率达到96.5%，分类阶段的分类准确率达到99.9%。整图测试的查准率为97.56%，查全率为99.26%。结论 相较于以往使用传统的目标检测模型进行货架商品识别以及使用SIFT等人工算子提取特征并分类识别商品具体SKU，文中方法的商品检出率和分类准确率都有了大幅度的提升，具有很好的应用潜力。 |
| 英文摘要: |
| The work aims to fast count the product information on shelves, and propose an automatic recognition method of shelf products based on deep neural network. The image of the shelf products collected by the camera was processed by the deep neural network algorithm to obtain the SKU and position of the products in the image. Aiming at the dense detection scenario of shelf product recognition, this method improved the general deep neural network object detection algorithm: the algorithm was divided into two stages of detection and classification and a part of the network structure was redesigned. At the same time, this method was compared with the traditional shelf product recognition methods and the general deep neural network object detection methods. From the experiment results, the average precision of the model reached 96.5% in the detection stage and 99% in the classification stage. In whole image test, the precision was 98.17% and the recall was 97.05%. Compared with prior works by traditional object detection methods for product recognition on shelves or SIFT artificial operators to extract features and classify product SKU, this method greatly improves the detection rate and classification precision rate, which has good application potential. |
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