LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
This work addresses brand recognition and intellectual property protection by providing a comprehensive dataset and applying deep learning, but it is incremental as it builds on existing object detection techniques.
The authors tackled the problem of logo detection and brand recognition by introducing LOGO-Net, a large-scale database with two datasets (logos-18 and logos-160) containing up to 130,608 logo objects, and applied deep region-based convolutional networks, achieving competitive results on new benchmarks.
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/