文献类型：期刊文献

英文题名：Intelligent Retail Settlement Platform based on Image Retrieval

作者：Yan, Xin[1]; Hu, Qingchun[1]; Huang, Xiaoyue[1]; Shen, Chen[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2022

起止页码：609

外文期刊名：2022 4th International Conference on Communications, Information System and Computer Engineering, CISCE 2022

收录：EI(收录号：20223612687612)

语种：英文

外文关键词：Ant colony optimization - Artificial intelligence - Distillation - Image retrieval - Sales

摘要：Automated Checkout (ACO) systems have high requirements for accuracy and speed in real retail scenarios. The research of ACO based on image recognition technology faces great challenges due to the lack of high-quality datasets, high merchandise granularity, and high model training costs. This project adopts PP-ShiTu algorithm based on mainbody detection, metric learning, and vector retrieval, and incorporates knowledge distillation and data enhancement strategies so as to carry out the implementation of merchandise recognition capability and effectively improve the prediction speed and recognition accuracy. With the feature learning dataset of retail scenes, the method adopted in this project can effectively balance the retrieval accuracy and speed, and improve the security and stability of the recognition process. Meanwhile, this project combines AIoT to connect 'cloud, edge and end', forming an integrated and intelligent retail settlement platform. ? 2022 IEEE.