Graphic Logo Detection with Deep Region-based Convolutional Networks

被引:0
作者
Li, Yuanyuan [1 ,2 ]
Shi, Qiuyue [1 ,2 ]
Deng, Jiangfan [1 ,2 ]
Su, Fei [1 ,2 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Commun & Informat Engn, Beijing, Peoples R China
[2] Beijing Univ Posts & Telecommun, Beijing Key Lab Network Syst & Network Culture, Beijing, Peoples R China
来源
2017 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP) | 2017年
关键词
Logo detection; Faster R-CNN; Data augmentation; Network modification;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Logo detection is a challenging task with many practical applications in our daily life and intellectual property protection. The two main obstacles here are lack of public logo datasets and effective design of logo detection structure. In this paper, we first manually collected and annotated 6,400 images and mix them with FlickrLogo-32 dataset, forming a larger dataset. Secondly, we constructed Faster R-CNN frameworks with several widely used classification models for logo detection. Furthermore, the transfer learning method was introduced in the training process. Finally, clustering was used to guarantee suitable hyper-parameters and more precise anchors of RPN. Experimental results show that the proposed framework outperforms the state of-the-art methods with a noticeable margin.
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页数:4
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