Detection of Bank Logos on Video using Faster R-CNN Method

Meryem Taşkesen, B. Ergen
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Abstract

In recent years, deep learning methods have achieved high success as solution to problems in the computer vision. Especially, CNN algorithms that extract information from the image is widely applied in logo detection. In this case, the recognition of trademark in trademark applications or infringement has been one of the major problems in the literature in terms of companies. In this paper, the dataset containing the logos of banks acquired from public domain images was collected in order to perform logo recognition, by using Faster R-CNN, an approach for the recognition of the bank logo in the video have been developed and as a result average accuracy of %98 was obtained.
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基于更快R-CNN方法的视频银行标识检测
近年来,深度学习方法在解决计算机视觉问题方面取得了很大的成功。特别是从图像中提取信息的CNN算法在logo检测中得到了广泛的应用。在这种情况下,商标申请或侵权中的商标识别问题一直是文献中对企业的主要问题之一。本文利用从公共领域图像中获取的银行徽标数据集进行徽标识别,利用Faster R-CNN开发了一种识别视频中银行徽标的方法,平均准确率达到98%。
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