基于稀疏卷积核的卷积神经网络研究及其应用

针对卷积神经网络训练图像数据时,其学习到的卷积核是杂乱无章,没有规则的,提出了基于稀疏卷积核的卷积神经网络算法。该方法通过对平方误差代价函数加入稀疏约束项,在反向传播中修正卷积核时,使其学习到的部分卷积核近似于一阶微分梯度算子,即学习到的卷积核中部分值是0或者趋于0,可更好地来提取图像边缘特征。通过对手语图像数据及车牌图像数据进行训练的实验结果显示,其学习到的部分卷积核具有近似一阶微分的模板形式;并且相对经典卷积神经网络,该算法的识别正确率有所提高。     

一种雷达辐射源智能个体识别的方法

针对使用传统的卷积神经网络及低信噪比环境下雷达辐射源智能个体识别研究中识别性能不够的问题,提出了一种基于短时傅里叶变换(STFT)和EfficientNet的雷达辐射源个体识别方法。首先对雷达信号进行短时傅里叶变换,提取时频特征,然后利用EfficientNet中多个MBconv模块对不同时频特征图像的叠加,挖掘出信号图像隐含的更加复杂和抽象的深层次时频特征,包括信号强度的分布、时频模式、周期性变化等,从而完成个体分类识别。EfficientNet可以同时改变网络深度、宽度、图像分辨率3个参数,解决了梯度消失、梯度爆炸等问题。实验结果表明,基于STFT和EfficientNet的雷达辐射源智能个体识别的方法,相比于传统卷积神经网络在低信噪比环境下具有更好的识别性能。     

低分辨率模糊车辆的人工智能识别研究

伴随着我国经济的飞速迅猛发展,国内各种汽车的数量增长飞快,道路交通治理将面临严峻的挑战。采用人工智能应对交通拥挤等各种状况是当前研究的热点。根据人工智能中经典的卷积神经网络,对远距离的低分辨率模糊车辆进行训练学习,提取图像的特点,得到图像分层的特征值。进一步采用区域卷积神经网络,从待识别的图片中定位识别出来车辆,实验结果表明识别准确率可以达到99%以上。该识别技术给智能交通系统提供了便捷的方案。     

基于深度学习的逆变器电路图像数据智能识别方法

进行逆变器电路图像数据识别时,特征信息提取不充分使得无法准确捕捉到关键特征,导致识别精度下降。为此,提出一种基于深度学习的逆变器电路图像数据智能识别方法。首先,利用逆变器数据采集系统,采集逆变器电路图像数据。然后,将图像数据输入到卷积神经网络模型中,通过卷积核提取数据的特征。最后,采用YOLO算法对其进行有效识别,基于CA模块对特征信息进行关注,并利用Detect模块输出识别结果。Detect模块主要包括置信度函数和模型的损失函数,将两者结合,利用分类框和检测框来实现对逆变器电路图像的识别。实验结果表明,所提方法的识别误报率最高仅为6%,具有实用性。     

基于卷积神经网络的复杂光照变化车牌图像识别

为解决车牌图像识别因复杂光照变化,导致车牌图像识别质量差的问题,提出基于卷积神经网络的复杂光照变化车牌图像识别方法。先采用复杂光照变化下车牌图像核心目标增强方法,对车牌图像核心目标进行有效聚类增强;再将复杂光照变化下核心目标增强后的车牌图像,作为基于深度可分离卷积网络的车牌图像识别方法的输入样本,导进卷积神经网络中,获取车牌图像特征图,然后将其变换为特征序列,通过双向循环神经网络,学习与预测车牌图像特征序列,实现对复杂光照变化下车牌图像的识别。实验结果表明,所提方法的识别精度高达0.99,比同类方法的识别精度高;在车牌图像数量逐渐增多时,该方法识别耗时仍低于2 s,识别效率显著。     

复杂电磁环境下基于信号时频图像的调制识别

为解决调制识别研究中较少考虑到不同信号的特征之间联系性的问题,搭建了卷积神经网络(CNN)来提取信号的彩色时频图对应的特征,并利用时频变换的分析方法,将一维信号处理成彩色时频图,通过卷积神经网络架构提取图像特征;同时为了提升算法在低信噪比下的分类识别准确率,对时频图像的纹理特征进行了特征提取,将提取到的纹理特征与卷积神...     

基于递进卷积神经网络的台标识别及其并行化

针对台标的视觉特征,提出一种基于递进卷积神经网络的台标识别算法.该网络不仅有对图像特征进行隐性提取的卷积层和采样层,还包括识别常规台标的泛化模块和识别偏差台标的特异模块.针对串行卷积神经网络训练耗时长的缺点,提出基于Spark的并行递进卷积神经网络算法,采用数据共享及批处理方式对算法模型进行并行化处理.实验证明,递进卷积神经网络算法对台标进行识别能达到98％的正确率,多节点并行化卷积神经网络相比于单节点模型能有效缩短80％以上训练所需的时间.     

基于特征分析的卷积神经网络烟雾识别

针对烟雾特征提取误差较高的问题,提出了一种烟雾特征与卷积神经网络结合(Characteristic Analysis Net_CNN,CAN_CNN)算法.CAN_CNN算法包含特征分析与目标识别两部分.特征分析部分主要利用烟雾的运动方向特征与颜色特征过滤烟雾图像中的其他运动物体,目标识别利用卷积神经网络获取烟雾深层次...     

基于深度卷积神经网络的图像分类识别方法

图像分类是通过图片所给的特征信息将不同的事物进行识别的一种图像处理技术。随着科学技术的快速发展以及人们对生活质量越来越高的需求,图像的自动分类技术已经运用到各个发展领域当中。当我们在图像上进行分类操作时,传统的图像分类方法由于不能准确掌握识别对象之间的内在联系,同时传统方法也因数据的特征性维度太高而导致识别对象的特征表达受到限制,所以取得的实验结果并不理想。针对以上内容文章提出了一种基于卷积神经网络的图像检测方法,该实验的算法主要借鉴了深度学习及卷积神经网络。与以往的传统图像分类方法不同,深度卷积神经网络模型可以同时进行特征学习和图像分类。通过对实验的各个部分结构进行改进和对卷积神经网络模型进行优化,从而防止过拟合现象,继而提高图像检测的准确度,在CIFAR-10数据库上进行的实验表明,该方法改进后的深度学习模型在图像检测方面取得了有效的结果。     

基于卷积神经网络的SAR图像目标识别研究

针对合成孔径雷达(Synthetic Aperture Radar, SAR)的图像目标识别应用, 该文提出了一种基于卷积神经网络(Convolutional Neural Network, CNN)的SAR图像目标识别方法。首先通过在误差代价函数中引入类别可分性度量, 提高了卷积神经网络的类别区分能力;然后利用改进后的卷积神经网络对SAR图像进行特征提取;最后利用支持向量机(Support Vector Machine, SVM)对特征进行分类。使用美国运动和静止目标获取与识别(Moving and Stationary Target Acquisition and Recognition, MSTAR)SAR图像数据进行实验, 识别结果证明了所提方法的有效性。      

基于改进卷积神经网络的交通标志识别方法

针对智能交通系统中小尺度交通标志识别率低的问题,文中提出一种改进卷积神经网络的交通标志识别方法。该方法通过在Faster R-CNN算法的低层特征图上增加优化的RPN网络,提升了小尺度交通标志的检测率。该方法还利用Max Pooling方法实了现图像的局部细节特征与全局语义特征充分融合。在TT-100K数据集上稍微实验结果表明新方法可以明显提高小尺度交通标志的识别率。     

卷积神经网络在脑疲劳检测中的研究

脑疲劳是由于持续进行脑力劳动导致的一种状态，脑电被认为是脑疲劳状态检测的最佳工具。如何选取合适的脑疲劳特征成为脑疲劳检测的关键问题，传统模式识别中手动提取特征会产生信息损失，针对脑电的时空特性，本文设计了具有时域卷积核、空间域卷积核的深层卷积神经网络和浅层卷积神经网络两种网络结构，将特征提取和状态分类合二为一，对正常态与疲劳态脑电数据进行分类，可视化了卷积神经网络的空间域卷积核。结果表明，浅层卷积神经网络平均分类正确率为98.868%，深层卷积神经网络平均分类正确率为98.217%，均高于传统分类方法，通过空间域卷积核的可视化，能够了解不同导联在网络中的参与程度，验证了该模型在脑疲劳检测任务中具有很高的有效性，同时为脑疲劳检测提供了新思路。      

Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis

With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, and feature extraction, in which classification accuracy is directly affected by the result of feature extraction. As a common deep learning method in image recognition, the convolutional neural network (CNN) demonstrates good performance in fault diagnosis. CNN can adaptively extract features from original signals and eliminate the effect of conventional handcrafted features. In this study, a multiscale learning neural network that contains one-dimension (1D) and two-dimension (2D) convolution channels is proposed. The network can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data. The Paderborn data set is came into use to demonstrate the classification accuracy of the method which is brought forward, which includes three conditions of healthy, outer ring (OR) damage and inner ring (IR) damage. The classification accuracy of the method which is put forward is up to 98.58%. The same dataset was applied to test the classification accuracy of support vector machine (SVM) for comparison. And the proposed multiscale learning neural network demonstrates considerable improvements.

     

Application of Artificial Neural Networks to Medical Image Pattern Recognition: Detection of Clustered Microcalcifications on Mammograms and Lung Cancer on Chest Radiographs

Three neural network models were employed to evaluate their performances in the recognition of medical image patterns associated with lung cancer and breast cancer in radiography. The first method was a pattern match neural network. The second was a conventional backpropagation neural network. The third method was a backpropagation trained neocognitron in which the signal propagation is operated with the convolution calculation from one layer to the next. In the convolution neural network (CNN) experiment, several output association methods and trainer imposed driving functions in conjunction with the convolution neural network are proposed for general medical image pattern recognition. An unconventional method of applying rotation and shift invariance is also used to enhance the performance of the neural nets.We have tested these methods for the detection of microcalcifications on mammograms and lung nodules on chest radiographs. Pre-scan methods were previously described in our early publications. The artificial neural networks act as final detection classifiers to determine if a disease pattern is presented on the suspected image area. We found that the convolution neural network, which internally performs feature extraction and classification, achieves the best performance among the three neural network models. These results show that some processing associated with disease feature extraction is a necessary step before a classifier can make an accurate determination.     

基于GAN和CNN-ELM的监控图像智能修复及检测方法

针对目前石化危险品装车过程中海量监控视频图像人为处理效率低下、模糊图像识别率低等问题,提出一种基于生成式对抗网络(GAN)和卷积神经网络(CNN)与极限学习机(ELM)相结合的监控模糊图像智能修复及检测方法.首先,使用深度学习网络作为 目标检测框架,利用GAN网络中生成器与判别器间的零和博弈对模糊图像进行复原,得到清晰完整的作业图像;其次,利用CNN自适应学习图像特征的能力,对修复后的图像进行自主特征提取;最后,将提取的图像特征输入ELM分类器中进行目标识别与分类,判断作业过程是否存在违规行为.试验结果表明:所提方法图像修复速度快,视觉效果自然,且目标识别准确率高,具有很好的泛化能力.     

Face Image Recognition Based on Convolutional Neural Network

With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a convolution sequence.Network deeper DCNN,channel number is bigger.The recognition rate of the model was verified by 0RL Face Database,BioID Face Database and CASIA Face Image Database.     

基于卷积神经网络的手势识别

手势识别是人机交互,智能语义识别和远程人机 交流领域的热门研究课题。目前基于 视觉的手势识别问题仍是研究的难点,在多变背景下的手势姿态识别仍然存在较大问题。近 年来,随着深度神经网络技术的快速发展,利用网络自主学习的方法来提取手势姿态有关特 征得到了广泛关注。由于卷积神经网络具有较强的学习能力和个体特征的表达能力,本文针 对传统手势识别算法精度低,鲁棒性差的问题,提出了基于卷积神经网络的TensorFlow框架 下加入扁平卷积模块的FD-CNN网络手势识别算法。在预处理数据集后,基于FD-CNN网络的 手 势识别方法可以直接将预处理后的图像输入网络进行训练,最终输出测试结果的识别精度为 99.0％。与传统方法和经典卷积神经网络方法相比,本文方法提高了 网 络系统对样本数据的多样性和复杂性的有效识别,具有较高的识别率和较好的鲁棒性效果。     

基于多层级深度神经网络的电力设备红外图像故障识别

电力设备的故障可能导致电力系统不稳定甚至解列,对电力安全和国民经济造成巨大损失,因此迅速且准确地识别这些故障至关重要。红外图像特征在捕捉发热故障的电力设备方面表现出良好的特征表达能力。然而,在图像采集过程中,可能会发生目标重叠、遮挡以及类目标干扰等问题。因此提出了一种复杂图像故障识别算法。基于多层级深度神经网络,充分利用多层网络模块的高层次特征提取能力和多级网络模块的特征融合能力,以提高故障识别的准确性。实验结果表明,该算法在准确率和运行时间等评估指标上优于现有的Faster-RCNN、VGG16、VGG19以及传统Resnet等模型,验证了其在解决图像中目标重叠、遮挡和类目标干扰等问题上的有效性。     

基于卷积神经网络的超声红外热图像分类

在超声红外热像技术应用中,从红外热图像来判断被测对象是否含有裂纹,通常需要先基于人工经验,从红外热图像中提取特征再采用某种模式识别方法进行分类,裂纹的识别与定位过程繁琐且识别率较低.为此,提出一种基于卷积神经网络技术的超声红外热图像裂纹检测与识别方法,其特点是可以直接从超声红外图像中学习特征进而实现是否含有裂纹红外热图...     

Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.