Banknote Recognition Research Based on Improved Deep Convolutional Neural Network

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.