LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

Hantang Liu; Huiqiong Wang; Hui Xue; Qiang Wu; Steven C.H. Hoi; Xiongwei Wu; Yue Wu

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

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arxiv 1511.02462 v2 pith:4746FH3L submitted 2015-11-08 cs.CV

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

Steven C.H. Hoi , Xiongwei Wu , Hantang Liu , Yue Wu , Huiqiong Wang , Hui Xue , Qiang Wu This is my paper

classification cs.CV

keywords logodetectiondeeplogo-netrecognitionbrandlarge-scaletechniques

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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/

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