Real-Time Barcode Detection and Classification using Deep Learning

Daniel Kold Hansen, Kamal Nasrollahi, Christoffer B. Rasmusen, Thomas B. Moeslund

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.

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Paper Citation


in Harvard Style

Hansen D., Nasrollahi K., B. Rasmusen C. and Moeslund T. (2017). Real-Time Barcode Detection and Classification using Deep Learning.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 321-327. DOI: 10.5220/0006508203210327


in Bibtex Style

@conference{ijcci17,
author={Daniel Kold Hansen and Kamal Nasrollahi and Christoffer B. Rasmusen and Thomas B. Moeslund},
title={Real-Time Barcode Detection and Classification using Deep Learning},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={321-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006508203210327},
isbn={978-989-758-274-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Real-Time Barcode Detection and Classification using Deep Learning
SN - 978-989-758-274-5
AU - Hansen D.
AU - Nasrollahi K.
AU - B. Rasmusen C.
AU - Moeslund T.
PY - 2017
SP - 321
EP - 327
DO - 10.5220/0006508203210327