Deep learning for logo detection
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Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
We present a deep learning system for automatic logo detection in real world images. We base our detector on the popular framework of FasterR-CNN and compare its performance to other models such as Mask R-CNN or RetinaNet. We perform a detailed empirical analysis of various design and architecture choices and show how these can have much higher influence than algorithmic tweaks or popular techniques such as data augmentation. We also provide a systematic detection performance comparison of various models on multiple popular datasets including FlickrLogos-32, TopLogo-10 and recently introduced QMUL-OpenLogo benchmark, which allows for a direct comparison between recently proposed extensions. By careful optimization of the training procedure we were able to achieve significant improvements of the state of the art on all mentioned datasets. We apply our observations to build a detector to detect logos of the Red Bull brand in online media and images.
Description
Subject(s)
Faster R-CNN, FlickrLogos-32, Logo detection, Mask R-CNN, QMUl-OpenLogo, RetinaNet, TopLogo-10