In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN). Experimental results on the banknote image databases of the Korean won (KRW) and the Indian rupee (INR) with three fitness levels, and the Unites States dollar (USD) with two fitness levels, showed that our method gives better classification accuracy than other methods.
The convolutional neural network (CNN)-based method proposed by Pham et al. REF could classify the fitness levels regardless of the denomination, and the input direction of banknotes in each of the currencies of INR, KRW and USD.
Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Medicine Computer Science Engineering