LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
Abstract
Logo detection from images has many applications, particularly for brand recognition and intellectual property protection. Most existing studies for logo recognition and detection are based on small-scale datasets which are not comprehensive enough when exploring emerging deep learning techniques. In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. To facilitate research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes, 10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo classes, 100 brands, and 130,608 logo objects. We describe the ideas and challenges for constructing such a large-scale database. Another key contribution of this work is to apply emerging deep learning techniques for logo detection and brand recognition tasks, and conduct extensive experiments by exploring several state-of-the-art deep region-based convolutional networks techniques for object detection tasks. The LOGO-net will be released at http://logo-net.org/
Cite
@article{arxiv.1511.02462,
title = {LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks},
author = {Steven C. H. Hoi and Xiongwei Wu and Hantang Liu and Yue Wu and Huiqiong Wang and Hui Xue and Qiang Wu},
journal= {arXiv preprint arXiv:1511.02462},
year = {2015}
}
Comments
15 pages