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Automatic detection of books based on Faster R-CNN

机译:基于更快的R-CNN自动检测书籍

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摘要

Advances have been made continuously in detection networks such as SPPnet and Fast R-CNN. Recently the novel region proposal method RPN shares full-image convolutional features with the detection network and enables a state-of-the-art object detection network Faster R-CNN. In this work we apply Faster R-CNN to train a detection network on our digital image database of books and implement automatic recognition and positioning of books. Experiments show that retrained Faster R-CNN achieves fine detection results in terms of both speed and accuracy, and it also solves the problem of testing negative examples in our previous study. This provides great help for the study of practical book retrieval system.
机译:在诸如SPPNET等检测网络中连续进行的进步和FAST R-CNN。最近,新颖的区域提议方法RPN利用检测网络共享全图像卷积特征,并使最先进的对象检测网络更快R-CNN。在这项工作中,我们申请更快的R-CNN来培训我们的数字图像数据库上的检测网络,实现书籍的自动识别和定位。实验表明,R-CNN的再升温速度达到速度和准确性的精细检测结果,并且还解决了在我们以前的研究中测试了负例的问题。这为实际书籍检索系统的研究提供了很大的帮助。

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