Engineering-oriented bridge multiple-damage detection with damage integrity using modified faster region-based convolutional neural network

Multimedia Tools and Applications - A bridge damage detector with preserving integrity based on modified Faster region-based convolutional neural network (R-CNN) is proposed for multiple damage...     

Quantum convolutional neural network for image classification

Pattern Analysis and Applications - In this paper we propose two scale-inspired local feature extraction methods based on Quantum Convolutional Neural Network (QCNN) in the Tensorflow quantum...     

采用卷积神经网络的低风险可行地貌分类方法

针对现有识别方法中风险地貌误判率高、手动地貌特征提取具有局限性等问题, 提出了用于室外移动机器 人的低风险地貌识别策略. 该策略以降低移动机器人遇险率为高优先级目标, 采用双重验证策略, 首先采用多分类 器对所有地貌进行识别, 其后使用二分类器对多分类结果中的安全地貌再次鉴别. 基于该策略, 分别设计了2个卷积 神经网络(CNN), Terrain–CNNⅠ用于多分类识别, Terrain–CNNⅡ则用于二分类安全确认. 为解决地貌样本相对稀缺 问题, 收集了包含水面、草地、泥地、柏油路、沙地、碎石路共6类地貌图像, 通过数据增强方式快速扩充数据集用于 网络的训练与测试. 实验结果表明: 所述方法在维持整体地貌识别率很高的前提下, 显著降低了关键危险地貌的误 判率.     

CT image classification based on convolutional neural network

Neural Computing and Applications - With the rapid development of the Internet, image information is explosively growing. Traditional image classification methods are difficult to deal with huge...     

Bi-branch deconvolution-based convolutional neural network for image classification

With the rise of deep neural network, convolutional neural networks show superior performances on many different computer vision recognition tasks. The convolution is used as one of the most efficient ways for extracting the details features of an image, while the deconvolution is mostly used for semantic segmentation and significance detection to obtain the contour information of the image and rarely used for image classification. In this paper, we propose a novel network named bi-branch deconvolution-based convolutional neural network (BB-deconvNet), which is constructed by mainly stacking a proposed simple module named Zoom. The Zoom module has two branches to extract multi-scale features from the same feature map. Especially, the deconvolution is borrowed to one of the branches, which can provide distinct features differently from regular convolution through the zoom of learned feature maps. To verify the effectiveness of the proposed network, we conduct several experiments on three object classification benchmarks (CIFAR-10, CIFAR-100, SVHN). The BB-deconvNet shows encouraging performances compared with other state-of-the-art deep CNNs.     

Improved convolutional neural network in remote sensing image classification

Neural Computing and Applications - The classification of land cover is the first step in the analysis and application of remote sensing data in land resources. How to solve the multi-category...     

The skin cancer classification using deep convolutional neural network

Multimedia Tools and Applications - This paper addresses the demand for an intelligent and rapid classification system of skin cancer using contemporary highly-efficient deep convolutional neural...     

Multi-label convolutional neural network based pedestrian attribute classification

Recently, pedestrian attributes like gender, age, clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database.     

基于卷积神经网络的语句级新闻分类算法

针对传统的中文文本分类在海量的互联网信息中难以胜任的现状,提出一种语句级的卷积神经网络中文新闻分类方案。通过信息提取算法从长短不一的新闻数据中提取固定大小的新闻摘要,压缩输入量的同时统一输入格式。信息提取时,通过对TF-IDF算法进行改进提升新闻摘要的质量,结合word2vec技术和卷积神经网络完成文本分类任务。与传统方法相比,词向量模型的引入弥补了传统词袋模型的缺陷,且语句的语义远比词的更加全面,使用语句进行分类更加可靠。通过实验对比验证了该方案具有较好的性能。     

基于卷积神经网络的加密流量分类方法

针对传统加密网络流量分类方法准确率较低、泛用性不强、易侵犯隐私等问题,提出了一种基于卷积神经网络的加密流量分类方法,避免依赖原始流量数据,防止过度拟合特定应用程序的字节结构。针对网络流量的数据包大小和到达时间信息,设计了一种将原始流量转换为二维图片的方法,直方图中每个单元格代表到达相应时间间隔的具有相应大小数据包的数量,不依赖数据包有效载荷,避免了侵犯隐私;针对LeNet-5卷积神经网络模型进行了优化以提高分类精度,嵌入Inception模块进行多维特征提取并进行特征融合,使用1*1卷积来控制输出的特征维度;使用平均池化层和卷积层替代全连接层,提高计算速度且避免过拟合;使用对象检测任务中的滑动窗口方法,将每个网络单向流划分为大小相等的块,确保单个会话中训练集中的块和测试集中的块没有重叠,扩充了数据集样本。在ISCX数据集上的分类实验结果显示,针对应用流量分类任务,准确率达到了95%以上。对比实验结果表明,训练集和测试集类型不同时,传统分类方法出现了显著的精度下降乃至失效,而所提方法的准确率依然达到了89.2%,证明了所提方法普适于加密流量与非加密流量。进行的所有实验均基于不平衡数据集,...     

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

(Aim) COVID-19 is an infectious disease spreading to the world this year. In this study, we plan to develop an artificial intelligence based tool to diagnose on chest CT images.(Method) On one hand, we extract features from a self-created convolutional neural network (CNN) to learn individual image-level representations. The proposed CNN employed several new techniques such as rank-based average pooling and multiple-way data augmentation. On the other hand, relation-aware representations were learnt from graph convolutional network (GCN). Deep feature fusion (DFF) was developed in this work to fuse individual image-level features and relation-aware features from both GCN and CNN, respectively. The best model was named as FGCNet.(Results) The experiment first chose the best model from eight proposed network models, and then compared it with 15 state-of-the-art approaches.(Conclusion) The proposed FGCNet model is effective and gives better performance than all 15 state-of-the-art methods. Thus, our proposed FGCNet model can assist radiologists to rapidly detect COVID-19 from chest CT images.     

基于卷积神经网络与多特征融合恶意代码分类方法

为了减小加壳、混淆技术对恶意代码分类的影响并提高准确率,提出一种基于卷积神经网络和多特征融合的恶意代码分类方法,以恶意代码灰度图像和带有API函数调用与操作码的混合序列为特征,设计基于卷积神经网络的多特征融合分类器。该分类器由图像组件、序列组件和融合组件构成,经训练后用于检测恶意代码类别。实验结果表明,相比目前已有的HYDRA、Orthrus等方法,该方法的分类准确率和宏F₁值更高,表明该方法能减小加壳、混淆技术影响,更准确地分类恶意代码。     

A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification

As one of the most important algorithms in the field of deep learning technology, the convolutional neural network (CNN) has been successfully applied in many fields. CNNs can recognize objects in an image by considering morphology and structure rather than simply individual pixels. One advantage of CNNs is that they exhibit translational invariance; when an image contains a certain degree of distortion or shift, a CNN can still recognize the object in the image. However, this advantage becomes a disadvantage when CNNs are applied to pixel-based classification of remote-sensing images, because their translational invariance characteristics causes distortions in land-cover boundaries and outlines in the classification result image. This problem severely limits the application of CNNs in remote-sensing classification. To solve this problem, we propose a central-point-enhanced convolutional neural network (CE-CNN) to classify high-resolution remote-sensing images. By introducing the central-point-enhanced layer when classifying a sample, the CE-CNN increases the weight of the central point in feather maps while preserving the original textures and characteristics. In our experiment, we selected four representative positions on a high-resolution remote-sensing image to test the classification ability of the proposed method and compared the CE-CNN with the traditional multi-layer perceptron (MLP) and a traditional CNN. The results show that the proposed method can not only achieves a higher classification accuracy but also less distortion and fewer incorrect results at the boundaries of land covers. We further compared the CE-CNN with six state-of-the-art methods: k-NN, maximum likelihood, classification and regression tree (CART), MLP, support vector machine, and CNN. The results show that the CE-CNN’s classification accuracy is better than the other methods.     

基于深度卷积神经网络的快速图像分类算法

为了应对大量图像的分类问题,提出一种基于深度卷积神经网络和CUDA-cuDNN并行运算的快速图像分类方法。该方法利用深度卷积神经网络自动学习特征的优势来解决手工设计特征普适性差等问题,同时结合基于CUDA架构的cuDNN并行运算策略来提高训练速度和加快分类速度,并且针对深度卷积神经网络易受参数扰动等缺点,引入批量正则化（Batch Normalization）以提高算法的鲁棒性。实验结果表明,该方法不仅大幅缩短了训练时间同时加快了图像的分类速度,而且进一步降低了图像分类的错误率。     

基于深度卷积神经网络的心音分类算法

针对现有心音分类算法普适性差、依赖于对基本心音的精确分割、分类模型结构单一等问题,提出采用大量未经过精确分割的心音二维特征图训练深度卷积神经网络(CNN)的方法;首先采用滑动窗口方法和梅尔频率系数对心音信号进行预处理,得到大量未经过精确分割的心音特征图;然后利用深度CNN模型对心音特征图进行训练和测试;根据卷积层间连接方式的不同,设计了 3种深度CNN模型:基于单一连接的卷积神经网络、基于跳跃连接的卷积神经网络、基于密集连接的卷积神经网络;实验结果表明,基于密集连接的卷积神经网络比其他两种网络具备更大的潜力;与其他心音分类算法相比,该算法不依赖于对基本心音的精确分割,且在分类准确率、敏感性和特异性方面均有提升.     

用于肺炎图像分类的优化卷积神经网络方法

目前,卷积神经网络（CNN）开始应用在肺炎分类领域。针对层数较浅、结构较为简单的卷积网络对肺炎识别的准确率难以提高的情况,采用深度学习方法,并针对采用深度学习方法时常常需要消耗大量的系统资源,导致卷积网络难以在用户端部署的问题,提出一种使用优化的卷积神经网络的分类方法。首先,根据肺炎图像的特征,选择具有良好图像分类性能的AlexNet与InceptionV3模型;然后,利用医学影像特点对层次更深、结构更加复杂的InceptionV3模型进行预训练;最后,通过知识蒸馏的方法,将训练好的"知识"（有效信息）提取到AlexNet模型中,从而实现在减少系统资源占用的同时,提高准确率的效果。实验数据表明,使用知识蒸馏后,AlexNet模型的准确率、特异性与灵敏度分别提高了4.1、7.45、1.97个百分点,且对图像处理器（GPU）占用相比InceptionV3模型减小了51个百分点。     

基于卷积神经网络的图像分类算法综述

卷积神经网络（CNN）是目前基于深度学习的计算机视觉领域中重要的研究方向之一。它在图像分类和分割、目标检测等的应用中表现出色,其强大的特征学习与特征表达能力越来越受到研究者的推崇。然而,CNN仍存在特征提取不完整、样本训练过拟合等问题。针对这些问题,介绍了CNN的发展、CNN经典的网络模型及其组件,并提供了解决上述问题的方法。通过对CNN模型在图像分类中研究现状的综述,为CNN的进一步发展及研究方向提供了建议。     

基于全双谱和卷积神经网络的信号分类方法

为解决电磁频谱中的未知信号分类和身份识别问题,提出一种基于改进卷积神经网络（CNN）LeNet-5模型的信号分类方法。该方法使用信号全双谱做为CNN的输入,然后通过改进的LeNet-5模型学习信号特征并完成信号分类和身份识别。实验结果表明,算法对未知信号调制类型识别率达97%以上,对信号身份属性识别率达96%以上。相比传统方法,该算法对信号身份属性识别率提高6.5%,具有更好的泛化性能,并有效解决了全双谱应用的二维模板匹配和Loss函数值下降缓慢问题。     

Active weighted mapping-based residual convolutional neural network for image classification

Multimedia Tools and Applications - In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using...     

Hyperspectral image classification via parallel multi-input mechanism-based convolutional neural network

In recent years, Convolutional Neural Networks (CNNs) have succeeded in Hyperspectral Image Classification and shown excellent performance. However, the implicit spatial information between features, which significantly affect the classification performance of CNNs, are neglected in most existing CNN models. To address this issue, we propose a parallel multi-input mechanism-based CNN (PMI-CNN) fully exploiting the implicit spectral-spatial information in Hyperspectral Images. PMI-CNN employs four parallel convolution branches to extract spatial features with different levels, feature maps from each branch are spliced, and used as the classifier’s input. The proposed PMI-CNN’s classification performance is examined on three benchmark datasets and compared with six competing models. Experimental results show that PMI-CNN has better classification performance via exploiting spectral-spatial information. Compared with other models, the classification accuracy of PMI-CNN on the Indian Pines dataset is significantly improved, varying between 1.23%-25.36%. Likewise, the PMI-CNN, performed on the other two benchmark datasets, achieves 0.54%-12.26% and 0.96%-8.38% advantages in overall accuracy over the other six models, respectively.