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An Intelligent Self-Service Vending System for Smart Retail.

Kun Xia1, Hongliang Fan1, Jianguang Huang1

  • 1Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Sensors (Basel, Switzerland)
|June 2, 2021
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Summary

This study introduces an intelligent vending system for un-barcoded items, using a single camera and deep learning for accurate real-time identification and payment. The system achieves high accuracy in weighing and product prediction, improving efficiency and reducing errors in retail.

Keywords:
internet of things (IoT)multi-device managementmulti-object detectionself-servicesmart retail

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Retail Technology

Background:

  • Traditional retail processes for non-barcode items are manual, inefficient, and error-prone.
  • There is a need for automated solutions to improve accuracy and reduce operational costs.

Purpose of the Study:

  • To develop an intelligent self-service vending system for non-barcode items.
  • To integrate real-time product detection, weighing, and online settlement.
  • To enhance the retail experience through automation and accuracy.

Main Methods:

  • Implementation of a single-camera embedded system for real-time, multi-product detection without labels.
  • Application of deep learning for product identification and real-time multi-object detection.
  • Development of a multi-device data management platform with seamless integration with payment systems (WeChat, Alipay) and databases.

Main Results:

  • Achieved a correlation coefficient (R²) of 0.99 between measured and actual weights.
  • Demonstrated a non-barcode item prediction accuracy of 93.73%.
  • Successfully validated system performance in diverse real-world sales scenarios.

Conclusions:

  • The proposed intelligent vending system effectively automates the weighing, identification, and settlement of non-barcode items.
  • The system offers a highly accurate and efficient solution for modern retail challenges.
  • The technology shows significant potential for improving customer self-service and operational efficiency in various sales environments.