基于视觉的钞票识别系统,采用不同的机器学习和深度学习方法

N. A. J. Sufri, N. A. Rahmad, N. F. Ghazali, N. Shahar, M. A. As’ari
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引用次数: 14

摘要

由于某些原因,视障人士在识别不同类型的纸币时遇到了困难。这一问题引起了研究人员的注意,他们引入了一种自动纸币识别系统,该系统可以分为基于视觉的系统和基于传感器的系统。本研究的主要目的是深入分析区域和方向对机器学习和深度学习性能的影响,分别使用马来西亚林吉特钞票(RM 1, RM 5, RM 10, RM 20, RM 50和RM 100)。在这个项目中,在一个受控的环境中,用手机摄像头对两种不同的区域和方向的纸币图像进行了两个实验。从不同区域的纸币图像中提取RGB值RB、RG和GB的特征,并将其应用于k-近邻(kNN)和决策树分类器(DTC)、支持向量机(SVM)和贝叶斯分类器(BC)等机器学习分类算法中,对每一类纸币进行识别。将不同方向的纸币图像直接输入深度学习神经网络中最流行的图像处理结构——卷积神经网络(CNN)的预训练模型AlexNet。采用十重交叉验证,选择交叉验证损失最小的优化kNN、DTC、SVM和BC。然后,在混淆矩阵中展示了kNN、DTC、SVM、BC和AlexNet模型的性能。kNN和DTC的准确率都达到了99.7%,而SVM和BC的准确率都达到了100%。这也可以得出结论,AlexNet只能在测试新数据时表现出色,如果数据之前已经接受过类似方向的训练。方向确实对AlexNet模型的性能有影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach
Visually impaired people faced a problem in identifying and recognizing the different types of banknote due to some reasons. This problem draws researchers’ attention to introduce an automated banknote recognition system that can be divided into a vision-based system and sensor-based system. The main aim of this study is to have deeper analysis on the effect of region and orientation on the performance of Machine Learning and Deep Learning respectively using Malaysian Ringgit banknotes (RM 1, RM 5, RM 10, RM 20, RM 50 and RM 100). In this project, two experiments conducted on two types of banknote image: different region and orientation captured by using handphone camera in a controlled environment. Feature extraction of the RGB values called RB, RG, and GB from banknote image with different region were used to the machine learning classification algorithms such as k-Nearest Neighbors (kNN) and Decision Tree Classifier (DTC), Support Vector Machine (SVM) and Bayesian Classifier (BC) for recognizing each class of banknote. Banknote image with different orientation was directly feed to AlexNet, a pre-trained model of Convolutional Neural Network (CNN), the most popular image processing structure of Deep Learning Neural Network. Ten-fold cross-validation was used to select the optimized kNN, DTC, SVM, and BC which was based on the smallest cross-validation loss. After that, the performance of kNN, DTC, SVM, BC and AlexNet model was presented in a confusion matrix. Both kNN and DTC achieved 99.7% accuracy but both SVM and BC perform better by succeeded to achieve 100% accuracy. It also can be concluded that AlexNet can only perform great in testing new data if only the data had previously been trained with similar orientation. Orientation does give effect to the performance of AlexNet model.
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