Logo Detection Using Deep Learning with Pretrained CNN Models

Authors

  • S. Sahel College of Computer and Information Systems, Umm Al Qura University, Saudi Arabia
  • M. Alsahafi College of Computer and Information Systems, Umm Al Qura University, Saudi Arabia
  • M. Alghamdi College of Computer and Information Systems, Umm Al Qura University, Saudi Arabia
  • T. Alsubait College of Computer and Information Systems, Umm Al Qura University, Saudi Arabia
Volume: 11 | Issue: 1 | Pages: 6724-6729 | February 2021 | https://doi.org/10.48084/etasr.3919

Abstract

Logo detection in images and videos is considered a key task for various applications, such as vehicle logo detection for traffic-monitoring systems, copyright infringement detection, and contextual content placement. The main contribution of this work is the application of emerging deep learning techniques to perform brand and logo recognition tasks through the use of multiple modern convolutional neural network models. In this work, pre-trained object detection models are utilized in order to enhance the performance of logo detection tasks when only a portion of labeled training images taken in truthful context is obtainable, evading wide manual classification costs. Superior logo detection results were obtained. In this study, the FlickrLogos-32 dataset was used, which is a common public dataset for logo detection and brand recognition from real-world product images. For model evaluation, the efficiency of creating the model and of its accuracy was considered.

Keywords:

logo detection, deep learning, convolutional neural networks, FlickrLogos-32

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How to Cite

[1]
S. Sahel, M. Alsahafi, M. Alghamdi, and T. Alsubait, “Logo Detection Using Deep Learning with Pretrained CNN Models”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 1, pp. 6724–6729, Feb. 2021.

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