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A Real-Time Cup-Detection Method Based on YOLOv3 for Inventory Management.

Wen-Sheng Wu1, Zhe-Ming Lu1

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Summary

This study introduces an improved YOLOv3 model for automated warehouse inventory management. The system accurately counts cups on shelves, reducing errors and enhancing efficiency for better inventory control.

Keywords:
YOLOv3deep learninginventory managementobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Operations Management

Background:

  • Traditional manual inventory management suffers from low efficiency and high labor costs.
  • Accurate inventory tracking is crucial for meeting consumer demand and minimizing storage expenses.
  • Automated solutions are needed to overcome the limitations of manual inventory processes.

Purpose of the Study:

  • To develop an automated inventory management system using an improved YOLOv3 model for detecting and counting cups on warehouse shelves.
  • To enhance the efficiency and accuracy of inventory management through computer vision techniques.
  • To provide significant data for inventory control by accurately counting items and tracking changes.

Main Methods:

  • Utilized an improved YOLOv3 object detection model for warehouse image analysis.
  • Optimized YOLOv3 by removing two smaller feature maps and using k-means clustering for anchor size optimization.
  • Implemented restricted detection areas to focus on relevant items and improve accuracy.
  • Collected warehouse images via camera and processed them on an industrial computer.

Main Results:

  • Reduced network parameters from 235 MB to 212 MB.
  • Improved detection frames per second (FPS) from 48.15 to 54.88.
  • Increased mean Average Precision (mAP) from 95.65% to 96.82%.
  • Achieved an average error rate of 1.61% in cup detection and counting.

Conclusions:

  • The improved YOLOv3 model significantly enhances automated inventory management accuracy and efficiency.
  • Optimizations in the YOLOv3 network architecture and anchor settings lead to better performance.
  • The system provides reliable data for effective inventory control, addressing the shortcomings of manual methods.