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Real-Time Barcode Detection and Classification using Deep Learning

Topics: Applications: Image and Signal Processing, Pattern Recognition, Decision Making, Human-Robot Interaction, Human Computer Interaction, Cognitive Robotics and Developmental Robotic, Sensor Mesh, Intelligent Networks, Internet Modeling, Multi-sensor Data Fusion using Computational Intelligence, Perceptual and Motor Functions (Visual, Auditory, Tactile, Virtual Reality, etc.), Intelligent Systems in Education; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications; Deep Learning

Authors: Daniel Kold Hansen ; Kamal Nasrollahi ; Christoffer B. Rasmusen and Thomas B. Moeslund

Affiliation: Aalborg University, Denmark

Keyword(s): Deep Learning, Barcode detection, Barcode Rotation.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 (IJCCI 2017) - IJCCI; ISBN 978-989-758-274-5; ISSN 2184-3236, SciTePress, pages 321-327. DOI: 10.5220/0006508203210327

@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 (IJCCI 2017) - IJCCI},
year={2017},
pages={321-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006508203210327},
isbn={978-989-758-274-5},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI
TI - Real-Time Barcode Detection and Classification using Deep Learning
SN - 978-989-758-274-5
IS - 2184-3236
AU - Hansen, D.
AU - Nasrollahi, K.
AU - B. Rasmusen, C.
AU - Moeslund, T.
PY - 2017
SP - 321
EP - 327
DO - 10.5220/0006508203210327
PB - SciTePress