大理大学学报 ›› 2019, Vol. 4 ›› Issue (12): 18-24.

• 数学与计算机科学 • 上一篇    下一篇

深度学习在泰文商品识别中的应用

  

  1. 云南民族大学云南省高校少数民族语言文字信息化处理工程研究中心,昆明650504
  • 收稿日期:2019-06-13 出版日期:2019-12-15 发布日期:2019-12-15
  • 通讯作者: 王嘉梅,教授,E-mail:wangj_2004@163.com
  • 作者简介:王清,硕士研究生,主要从事边疆语言文化与应用研究.
  • 基金资助:
    国家语委科研基金资助项目(WT125-61);云南省教育厅科学研究基金资助项目(2019Y0223)

Application of Deep Learning in Thai Commodity Recognition

  1. Yunnan Provincial Minority Language Information Processing Engineering Research Center, Yunnan Minzu University,
    Kunming 650504, China
  • Received:2019-06-13 Online:2019-12-15 Published:2019-12-15

摘要: 跨境零售是中泰贸易的关键,但传统采用RFID标签识别和人工问询模式的购物体验度并不高。因而运用基于机器
视觉的商品识别方法,构建“互联网+跨境贸易”的人工智能化零售电商体系,对推动中泰“一带一路”的区域经济发展具有
重要的应用价值。该文在图像分类识别技术和卷积神经网络研究的基础上,提出一种基于Inception-V3框架参数迁移学习
的Inception-Thai神经网络模型来用于泰文商品的识别,可避免在少量样本上出现训练过拟合的现象。在人工标注的7类
泰文商品图片数据集上进行测试,实验结果表明,该模型具有较高的图片深层特征提取能力,且在商品识别的分类置信度
上可达到83%至98%,取得了较高的准确率。

关键词: 跨境零售, 识别, 深度学习, 图像分类

Abstract: Cross-border retail is the key to Sino-Thai trade, but the traditional shopping experience using RFID tag recognition and
manual inquiry mode is not satisfactory. Therefore, the use of machine vision-based commodity identification method to build an
artificial intelligence retail e-commerce system of "Internet + cross-border trade" has important application value for promoting the
regional economic development of the China-Thailand "Belt and Road". Based on the research of image classification and recognition
technology and convolution neural network, this paper proposes an Inception-Thai neural network model on the basis of Inception-V3
framework parameter transfer learning for Thai commodity recognition, which can avoid over-fitting in a small number of samples. The
experimental results on seven types of Thai merchandise image datasets show that the model has a high ability to extract deep-seated
features of images, and the classification confidence of merchandise recognition can reach 83% to 98%, which achieves a high
accuracy.

Key words: cross-border retail, identification, deep learning, image classification