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Deep learning framework for barcode localization and decoding using simulated UAV imagery.

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This study introduces an automated warehouse inventory system using Unmanned Aerial Vehicles (UAVs) for barcode scanning. The deep learning framework achieves high accuracy in detecting barcodes, improving logistics efficiency.

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Area of Science:

  • Logistics and Supply Chain Management
  • Computer Vision
  • Robotics

Background:

  • Automated warehouse stock tracking is crucial for logistics efficiency and error reduction.
  • Unmanned Aerial Vehicles (UAVs) present a viable solution for automated aerial barcode scanning.
  • Real-time barcode detection faces challenges like poor lighting, shadows, and occlusions.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for automated barcode inventory management using simulated UAV imagery.
  • To enhance the reliability of barcode detection and decoding in complex warehouse environments.
  • To demonstrate a drone-ready system for real-world UAV deployment.

Main Methods:

  • Utilized the YOLOv8 object detection model for accurate localization of 1D and 2D barcodes from a UAV perspective.
  • Employed OpenCV's barcode module for decoding localized barcode regions.
  • Integrated extracted data into a MySQL database for simulated real-time stock updates.

Main Results:

  • Achieved a mean Average Precision (mAP) of 92.4% for barcode detection, indicating strong performance in challenging conditions.
  • Successfully localized and decoded barcodes from simulated UAV imagery.
  • Demonstrated a functional pipeline from image capture to database update.

Conclusions:

  • The proposed deep learning framework shows significant potential for reliable automated warehouse inventory management via UAVs.
  • The system's modular design facilitates drone-ready deployment with minimal adjustments.
  • This approach can substantially reduce human effort and errors in inventory tracking processes.