Lightweight commodity identification tracking system, method, storage medium, device, terminal

By deploying a lightweight product recognition and tracking system on edge intelligent devices, and optimizing feature extraction and the DIOU_NMS algorithm using the Ghost Module and RFP modules, the problems of low recognition accuracy and high deployment cost in unmanned vending scenarios are solved, achieving fast and accurate product recognition and tracking, and supporting seamless shopping.

CN115311450BActive Publication Date: 2026-04-28OCEAN UNIV OF CHINA +1
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Patent Information

Application Number
CN202210881030.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-07-26
Publication Date
2026-04-28
Estimated Expiration
2042-07-26

AI Technical Summary

Technical Problem

In unattended vending scenarios, existing product recognition technologies suffer from environmental complexity, high deployment costs, network fluctuations leading to prolonged recognition time and low accuracy, especially in the recognition of small targets, occlusions, and multiple targets.

Method used

A lightweight feature extraction network is designed using the Ghost Module, combined with the RFP multi-scale detection module and the DIOU_NMS algorithm to optimize the product recognition and tracking algorithm. The lightweight product recognition and tracking system is then deployed on the edge intelligent device Jeston Nano to achieve rapid recognition and tracking.

Benefits of technology

It significantly improves the accuracy and speed of product recognition on edge smart devices, reduces recognition time, supports a seamless shopping process, lowers deployment costs, and expands the application scope of unmanned vending.

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Abstract

The present application belongs to the technical field of neural network lightweight design and target detection tracking neural network fast inference framework, and discloses a lightweight commodity identification and tracking system, method, storage medium, equipment and terminal, which collects and labels commodity identification training data set through installation of picture collection facilities in actual intelligent vending scene; designs a backbone feature extraction network based on Ghost Module, designs a multi-scale commodity detection module based on RFP, and designs a multi-target detection and target occlusion identification module based on DIOU_NMS algorithm; adds a commodity tracking algorithm in the lightweight commodity identification network to analyze the behavior of the user, and discriminates the user's taking and abnormal operation based on continuous key frames. The present application provides a lightweight commodity identification and tracking fusion network, which can provide suggestion guidance for embedded application of network deployment in edge intelligent equipment in corresponding scenes.
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