基于Faster R-CNN的图书自动检测

Beibei Zhu, Xiaoyu Wu, Lei Yang, Yinghua Shen, Linglin Wu
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引用次数: 15

摘要

SPPnet、Fast R-CNN等检测网络不断取得进展。最近,新的区域建议方法RPN与检测网络共享全图像卷积特征,使最先进的目标检测网络更快。在这项工作中,我们使用Faster R-CNN在我们的图书数字图像数据库上训练检测网络,实现图书的自动识别和定位。实验表明,经过再训练的Faster R-CNN在速度和准确率上都取得了很好的检测效果,也解决了我们之前研究中存在的测试反例的问题。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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Automatic detection of books based on Faster R-CNN
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.
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