Real-Time Barcode Detection and Classification Using Deep Learning

Daniel Kold Hansen, Kamal Nasrollahi, Christoffer Bøgelund Rasmussen, Thomas B. Moeslund

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

59 Citations (Scopus)
11545 Downloads (Pure)

Abstract

Barcodes, in their different forms, can be found on almost any packages available in the market. Detecting and then decoding of barcodes have therefore great applications. We describe how to adapt the state-of-the- art deep learning-based detector of You Only Look Once (YOLO) for the purpose of detecting barcodes in a fast and reliable way. The detector is capable of detecting both 1D and QR barcodes. The detector achieves state-of-the-art results on the benchmark dataset of Muenster BarcodeDB with a detection rate of 0.991. The developed system can also find the rotation of both the 1D and QR barcodes, which gives the opportunity of rotating the detection accordingly which is shown to benefit the decoding process in a positive way. Both the detection and the rotation prediction shows real-time performance.
Original languageEnglish
Title of host publicationProceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI
Volume1
PublisherSCITEPRESS Digital Library
Publication date2017
Pages321-327
ISBN (Print)978-989-758-274-5
DOIs
Publication statusPublished - 2017
EventInternational Joint Conference on Computational Intelligence - Funchal, Portugal
Duration: 1 Nov 20173 Nov 2017
Conference number: 9
http://www.ijcci.org/

Conference

ConferenceInternational Joint Conference on Computational Intelligence
Number9
Country/TerritoryPortugal
CityFunchal
Period01/11/201703/11/2017
Internet address

Keywords

  • Barcode detection
  • Barcode rotation

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