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Resumé

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.
OriginalsprogEngelsk
TitelProceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI
Vol/bind1
ForlagSCITEPRESS Digital Library
Publikationsdato2017
Sider321-327
ISBN (Trykt)978-989-758-274-5
DOI
StatusUdgivet - 2017
BegivenhedInternational Joint Conference on Computational Intelligence - Funchal, Portugal
Varighed: 1 nov. 20173 nov. 2017
Konferencens nummer: 9
http://www.ijcci.org/

Konference

KonferenceInternational Joint Conference on Computational Intelligence
Nummer9
LandPortugal
ByFunchal
Periode01/11/201703/11/2017
Internetadresse

Fingeraftryk

Detectors
Decoding
Deep learning

Emneord

    Citer dette

    Hansen, D. K., Nasrollahi, K., Rasmussen, C. B., & Moeslund, T. B. (2017). Real-Time Barcode Detection and Classification Using Deep Learning. I Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI (Bind 1, s. 321-327). SCITEPRESS Digital Library. https://doi.org/10.5220/0006508203210327
    Hansen, Daniel Kold ; Nasrollahi, Kamal ; Rasmussen, Christoffer Bøgelund ; Moeslund, Thomas B. / Real-Time Barcode Detection and Classification Using Deep Learning. Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI. Bind 1 SCITEPRESS Digital Library, 2017. s. 321-327
    @inproceedings{50c7489d486e4cfe868358b883dc2dbc,
    title = "Real-Time Barcode Detection and Classification Using Deep Learning",
    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.",
    keywords = "Barcode detection, Barcode rotation",
    author = "Hansen, {Daniel Kold} and Kamal Nasrollahi and Rasmussen, {Christoffer B{\o}gelund} and Moeslund, {Thomas B.}",
    year = "2017",
    doi = "10.5220/0006508203210327",
    language = "English",
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    booktitle = "Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI",
    publisher = "SCITEPRESS Digital Library",

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    Hansen, DK, Nasrollahi, K, Rasmussen, CB & Moeslund, TB 2017, Real-Time Barcode Detection and Classification Using Deep Learning. i Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI. bind 1, SCITEPRESS Digital Library, s. 321-327, Funchal, Portugal, 01/11/2017. https://doi.org/10.5220/0006508203210327

    Real-Time Barcode Detection and Classification Using Deep Learning. / Hansen, Daniel Kold; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund; Moeslund, Thomas B.

    Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI. Bind 1 SCITEPRESS Digital Library, 2017. s. 321-327.

    Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

    TY - GEN

    T1 - Real-Time Barcode Detection and Classification Using Deep Learning

    AU - Hansen, Daniel Kold

    AU - Nasrollahi, Kamal

    AU - Rasmussen, Christoffer Bøgelund

    AU - Moeslund, Thomas B.

    PY - 2017

    Y1 - 2017

    N2 - 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.

    AB - 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.

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    KW - Barcode rotation

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    M3 - Article in proceeding

    SN - 978-989-758-274-5

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    Hansen DK, Nasrollahi K, Rasmussen CB, Moeslund TB. Real-Time Barcode Detection and Classification Using Deep Learning. I Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI. Bind 1. SCITEPRESS Digital Library. 2017. s. 321-327 https://doi.org/10.5220/0006508203210327