Logo Detection Using Deep Learning with Pretrained CNN Models
Received: 1 November 2020 | Revised: 10 December 2020 and 23 December 2020 | Accepted: 2 January 2021 | Online: 6 February 2021
Corresponding author: T. Alsubait
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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