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Automatic detection of books based on Faster R-CNN

机译:基于Faster R-CNN的书籍自动检测

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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和Fast R-CNN的检测网络中,不断取得了进步。最近,新颖的区域提议方法RPN与检测网络共享全图像卷积特征,并启用了最新的对象检测网络Faster R-CNN。在这项工作中,我们应用Faster R-CNN在我们的书籍数字图像数据库上训练一个检测网络,并实现书籍的自动识别和定位。实验表明,经过再训练的Faster R-CNN在速度和准确性方面均取得了很好的检测结果,并且还解决了我们先前研究中测试负面样本的问题。这为实用图书检索系统的研究提供了很大的帮助。

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