Logo Recognition with the Use of Deep Convolutional Neural Networks

Authors

  • A. Alsheikhy Electrical Engineering Department, College of Engineering, Northern Border University, Saudi Arabia
  • Y. Said Electrical Engineering Department, Northern Border University, Saudi Arabia | Faculty of Sciences of Monastir, University of Monastir, Tunisia https://orcid.org/0000-0003-0613-4037
  • M. Barr Electrical Engineering Department, Northern Border University, Saudi Arabia
Volume: 10 | Issue: 5 | Pages: 6191-6194 | October 2020 | https://doi.org/10.48084/etasr.3734
Corresponding author: Y. Said

Abstract

Automatic logo recognition is gaining importance due to the increasing number of its applications. Unlike other object recognition tasks, logo recognition is more challenging because of the limited amount of the available original data. In this paper, the transfer leaning technique was applied to a Deep Convolutional Neural Network model to guarantee logo recognition using a small computational overhead. The proposed method was based on the Densely Connected Convolutional Networks (DenseNet). The experimental results show that for the FlickrLogos-32 logo recognition dataset, our proposed method performs comparably with state-of-the-art methods while using fewer parameters.

Keywords:

logo recognition, deep learning, Convolutional Neural Networks (CNNs), DenseNet, artificial intelligence

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How to Cite

[1]
A. Alsheikhy, Y. Said, and M. Barr, “Logo Recognition with the Use of Deep Convolutional Neural Networks”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 5, pp. 6191–6194, Oct. 2020.

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