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.
Highlights
Logo Detection (LD) is important for several real-world applications [1] by enhancing the ability of users/systems to recognize the identities of items by their brand logos
This paper presents the current LD methods and sheds light on common datasets used in previous research
The increased accuracy comes with a cost. This is because the FR-Convolutional Neural Networks (CNNs) first applies CNN and the zones where compared to the RCNN which makes the regions first and applies CNN
Summary
Logo Detection (LD) is important for several real-world applications [1] by enhancing the ability of users/systems to recognize the identities of items by their brand logos. LD is a sub-field of object detection which is the task of identifying wherever there is a specific object in an image. There has been a growing interest in using Convolutional Neural Networks (CNNs) to perform object detection tasks. This approach usually starts by capturing images by camera devices, probably experimenting with different resolutions, and processing the images in order to be able to classify the images of the objects contained within [3]. Three steps can be used in traditional object detection and classification approaches, and these are: informative region selection, feature extraction, and classification [4]. It is probable to scan the whole image consuming a multi-scale sliding window, as numerous substances (objects) might appear in different positions with several sizes and feature ratios
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