Banknote Recognition Research Based on Improved Deep Convolutional Neural Network

Shan GAI; Zhongyun BAO

doi:10.11999/JEIT181097

Volume 41 Issue 8

Aug. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(8): 1992-2000

Shan GAI, Zhongyun BAO. Banknote Recognition Research Based on Improved Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1992-2000. doi: 10.11999/JEIT181097

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Shan GAI, Zhongyun BAO. Banknote Recognition Research Based on Improved Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1992-2000. doi: 10.11999/JEIT181097

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Banknote Recognition Research Based on Improved Deep Convolutional Neural Network

doi: 10.11999/JEIT181097

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)

Received Date: 2018-11-28
Rev Recd Date: 2019-03-27

Available Online: 2019-04-21

Publish Date: 2019-08-01

Abstract

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.
- Banknote recognition,
- Deep Convolutional Neural Network (DCNN),
- Residual learning,
- Spatial pyramid pooling

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References(18)

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