鲍中运
南昌航空大学信息工程学院 南昌 330063
基金项目:国家自然科学基金(61563037)，江西省****计划(20171BCB23057)
详细信息
作者简介:盖杉：男，1980年生，副教授，硕士生导师，研究方向为计算机视觉、图像处理、深度学习
鲍中运：男，1990年生，硕士生，研究方向为计算机视觉、图像处理、深度学习
通讯作者:盖杉 gaishan@nchu.edu.cn
中图分类号:TP391.41; TP181计量
文章访问数:3106
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被引次数:0
出版历程
收稿日期:2018-11-28
修回日期:2019-03-27
网络出版日期:2019-04-21
刊出日期:2019-08-01
Banknote Recognition Research Based on Improved Deep Convolutional Neural Network
Shan GAI,,Zhongyun BAO
School ofInformation Engineering, Nanchang HangkongUniversity, Nanchang 330063, China
Funds:The National Natural Science Foundation of China(61563037), The Outstanding Youth Scheme of Jiangxi Province (20171BCB23057)
摘要
摘要:针对如何提高纸币识别率的问题，该文提出一种改进深度卷积神经网络(DCNN)的纸币识别算法。该算法首先通过融合迁移学习、带泄露整流(Leaky ReLU)函数、批量归一化(BN)和多层次残差单元构造深度卷积层，对输入的不同尺寸纸币进行稳定而快速的特征提取与学习；然后采用改进的多层次空间金字塔池化算法对提取的纸币特征实现固定大小的输出表示；最后通过网络全连接层和softmax层实现纸币图像分类。实验结果表明，该算法在分类性能、泛化能力与稳定性上明显优于常用的纸币分类算法；同时该算法也能够满足纸币清分系统的实时性要求。
关键词:纸币识别/
深度卷积神经网络/
残差学习/
空间金字塔池化
Abstract:In order to improve the recognition rate of banknotes, the improved banknote recognition algorithm based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, the algorithm constructs a deep convolution layer by integrating transfer learning, Leaky-Rectified Liner Unit (Leaky ReLU) function, Batch Normalization(BN) and multi-level residual unit that perform stable and fast feature extraction and learning on input different size banknotes. Secondly, a fixed-size output representation of the extracted banknote features is obtained by using the improved multi-level spatial pyramid pooling algorithm. Finally, the banknote classification is implemented by the full connection layer and the softmax layer of the network. The experimental results show that the proposed algorithm can effectively improve the recognition rate of banknotes, and has better generalization ability and robustness than the traditional banknote classification method. Meanwhile, the algorithm can meet the real-time requirements of the banknote sorting system.
Key words:Banknote recognition/
Deep Convolutional Neural Network (DCNN)/
Residual learning/
Spatial pyramid pooling
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https://jeit.ac.cn/article/exportPdf?id=c9fa8008-f3e2-4c78-bab5-933e0a89c81e
