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

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Název: Deep learning framework for barcode localization and decoding using simulated UAV imagery.
Autoři: Alsulami F; Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, 23890, Jeddah, Saudi Arabia. fnalsulami@uj.edu.sa., Jhanjhi NZ; School of Computer Science, Taylor's University, Subang Jaya, Malaysia.; Office of Research and Development, Asia University, Taichung, Taiwan.
Zdroj: Scientific reports [Sci Rep] 2025 Dec 03. Date of Electronic Publication: 2025 Dec 03.
Publication Model: Ahead of Print
Způsob vydávání: Journal Article
Jazyk: English
Informace o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s): Original Publication: London : Nature Publishing Group, copyright 2011-
Abstrakt: Automated warehouse stock tracking is becoming increasingly important for improving logistics and reducing manual errors. Unmanned Aerial Vehicles (UAVs) offer a promising solution by enabling automated barcode scanning from above. However, challenges such as poor lighting, shadows, and partial occlusions still limit the reliability of real-time barcode detection and decoding. This research presents a deep learning framework evaluated on simulated UAV imagery for barcode inventory management. The proposed system uses the YOLOv8 object detection model to accurately localize both 1D and 2D barcodes in images captured from a UAV perspective. With a mean Average Precision (mAP) of 92.4%, the model demonstrates strong performance even in complex warehouse conditions. Once localized, the barcode regions are decoded using OpenCV's barcode module. The extracted data, including product ID and quantity, is then automatically updated into a MySQL database to simulate real-time stock updates. Although tested using simulated aerial imagery, the system is designed to be drone-ready with minimal adjustments. This modular approach shows potential for real-world UAV deployment and contributes to reducing human effort and errors in inventory tracking.
(© 2025. The Author(s).)
Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
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Entry Date(s): Date Created: 20251203 Latest Revision: 20251203
Update Code: 20251204
DOI: 10.1038/s41598-025-29720-w
PMID: 41339720
Databáze: MEDLINE
Popis
Abstrakt:Automated warehouse stock tracking is becoming increasingly important for improving logistics and reducing manual errors. Unmanned Aerial Vehicles (UAVs) offer a promising solution by enabling automated barcode scanning from above. However, challenges such as poor lighting, shadows, and partial occlusions still limit the reliability of real-time barcode detection and decoding. This research presents a deep learning framework evaluated on simulated UAV imagery for barcode inventory management. The proposed system uses the YOLOv8 object detection model to accurately localize both 1D and 2D barcodes in images captured from a UAV perspective. With a mean Average Precision (mAP) of 92.4%, the model demonstrates strong performance even in complex warehouse conditions. Once localized, the barcode regions are decoded using OpenCV's barcode module. The extracted data, including product ID and quantity, is then automatically updated into a MySQL database to simulate real-time stock updates. Although tested using simulated aerial imagery, the system is designed to be drone-ready with minimal adjustments. This modular approach shows potential for real-world UAV deployment and contributes to reducing human effort and errors in inventory tracking.<br /> (© 2025. The Author(s).)
ISSN:2045-2322
DOI:10.1038/s41598-025-29720-w