基于卷积神经网络图像分类优化算法的研究与验证

发布时间：2018-04-20 00:15

本文选题：卷积神经网络 + 激活函数　；参考：《北京交通大学》2017年硕士论文

【摘要】：卷积神经网络属于深度学习领域研究的范围,是一种高效的识别方法,卷积神经网络具有三个特点分别为参数共享,局部感知和子采样操作,这三个特点使得训练参数减少,训练速度加快,在训练过程中具有良好表现,目前卷积神经网络已经广泛的并且良好的应用在生活各个方面,特别是在图像分类任务,语音识别,文本识别,路标识别等方面。但其发展过程中还存在一些问题。本文将对卷积神经网络在图像分类领域进行研究,目的是希望提高图像分类的精准率,降低错误率。激活函数通过非线性函数把激活的神经元的特征保留并映射出来,因此对于网络性能有很大的影响,但是目前激活函数的选择是一个问题,不同的激活函数具有不同的优缺点,需要耗费大量的时间与精力来确定最优的激活函数。本文主要针对激活函数选择困难的问题,提出基于Relu-Softplus激活函数的卷积神经网络,并在手写数字字体MNIST数据集上进行实验,加以验证其性能,并且同其他不同的激活函数进行比对,分析其图像分类的错误率,以及收敛速度的快慢,最终达到优化卷积神经网络的性能和解决确定最优激活函数困难等问题的目的。卷积神经网络中的学习方式常见的有两种,有监督学习方法和无监督学习方法,有监督学习即从已标记的训练样本中学习到映射函数,但是需要大量的训练样本,并且易出现过拟合等问题。而无监督学习不要求训练样本带有标签,希望学习到更过抽象隐藏的特征结构,但具有训练时间长,训练过程繁琐等缺点。本文主要针对此问题,提出基于K-means算法的卷积神经网络,并在CIFAR-10数据集上进行实验,加以验证其性能,并分析比较不同的网络框架对图像分类精准率的影响。最后本论文将卷积神经网络应用在路标识别系统上,并且设计了一个路标识别系统,从系统的需求分析,概要设计,详细设计以实现等方面进行了阐述。并将本文提出的基于K-means算法的卷积神经网络应用在路标识别系统中,最后在德国交通标志识别GTRSB数据集上进行训练测试,并同其他知名的算法进行比较,加以验证了基于K-means算法的卷积神经网络在路标识别系统的应用中对于路标分类的准确性,可靠性以及时效性方面确实有一定的提升。
[Abstract]:Convolutional neural network is an efficient recognition method, which belongs to the field of deep learning. It has three characteristics: parameter sharing, local sensing and sub-sampling operation, which make the training parameters reduced. At present, convolution neural network has been widely used in all aspects of life, especially in image classification task, speech recognition, text recognition, road sign recognition and so on. However, there are still some problems in its development. In this paper convolution neural networks are studied in the field of image classification in order to improve the accuracy of image classification and reduce the error rate. The activation function preserves and maps the characteristics of the activated neuron through the nonlinear function, so it has a great influence on the network performance. But at present, the choice of the activation function is a problem, and different activation functions have different advantages and disadvantages. It takes a lot of time and effort to determine the optimal activation function. Aiming at the difficulty of selecting activation function, a convolutional neural network based on Relu-Softplus activation function is proposed in this paper. Experiments are carried out on the MNIST dataset of handwritten digital font to verify its performance. Compared with other activation functions, the error rate of image classification and the speed of convergence are analyzed. Finally, the performance of convolution neural network is optimized and the problem of determining the optimal activation function is solved. There are two common learning methods in convolutional neural networks: supervised learning and unsupervised learning. Supervised learning is learning mapping functions from marked training samples, but a large number of training samples are required. And easy to have problems such as fitting. But the unsupervised learning does not require the training samples to be labeled, hoping to learn more abstract and hidden feature structures, but it has the disadvantages of long training time and tedious training process. In order to solve this problem, a convolutional neural network based on K-means algorithm is proposed in this paper. Experiments are carried out on the CIFAR-10 dataset to verify its performance, and the effects of different network frameworks on the accuracy rate of image classification are analyzed and compared. Finally, this paper applies the convolution neural network to the signpost recognition system, and designs a signpost recognition system, which is described from the aspects of system requirement analysis, summary design, detailed design and so on. The convolutional neural network based on K-means algorithm is applied to the road sign recognition system. Finally, the training test is carried out on the GTRSB data set of traffic sign recognition in Germany, and compared with other well-known algorithms. It is verified that convolution neural network based on K-means algorithm can improve the accuracy, reliability and timeliness of road sign classification in the application of road sign recognition system.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183

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