This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain classification.VGG-Net has a strong ability of deep ...This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain classification.VGG-Net has a strong ability of deep feature extraction,which can fully extract the global deep features of different terrains in PolSAR images,so it is widely used in PolSAR terrain classification.However,VGG-Net ignores the local edge & shape features,resulting in incomplete feature representation of the PolSAR terrains,as a consequence,the terrain classification accuracy is not promising.In fact,edge and shape features play an important role in PolSAR terrain classification.To solve this problem,a new VGG network with HOG feature fusion was specifically proposed for high-precision PolSAR terrain classification.HOG-VGG extracts both the global deep semantic features and the local edge & shape features of the PolSAR terrains,so the terrain feature representation completeness is greatly elevated.Moreover,HOG-VGG optimally fuses the global deep features and the local edge & shape features to achieve the best classification results.The superiority of HOG-VGG is verified on the Flevoland,San Francisco and Oberpfaffenhofen datasets.Experiments show that the proposed HOG-VGG achieves much better PolSAR terrain classification performance,with overall accuracies of 97.54%,94.63%,and 96.07%,respectively.展开更多
针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了...针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.展开更多
重大装备制造中厚板机器人多层多道焊(multi-layer and multi-pass welding,MLMPW)一直是热点和难点,而实现机器人MLMPW的核心是对其熔池的获取、监控并分类.为了提高MLMPW的自动化和智能化,有必要开发一个熔池图像在线分类系统.针对焊...重大装备制造中厚板机器人多层多道焊(multi-layer and multi-pass welding,MLMPW)一直是热点和难点,而实现机器人MLMPW的核心是对其熔池的获取、监控并分类.为了提高MLMPW的自动化和智能化,有必要开发一个熔池图像在线分类系统.针对焊接过程中的熔池图像提出了一种新的MLMPW熔池分类方法——基于视觉注意的(SENet)VGGNet熔池分类方法.为了提高效率和精度,引入迁移学习中的预训练模型到网络训练过程中.因为针对中厚板多层多道熔池研究较少,导致熔池公开数据集较少,为了应对这一问题,需要对数据集进行增广.结果表明,提出的模型可快速有效的对七类MLMPW熔池进行准确分类,预测精度可达到98.39%.展开更多
Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the ...Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.展开更多
基金Sponsored by the Fundamental Research Funds for the Central Universities of China(Grant No.PA2023IISL0098)the Hefei Municipal Natural Science Foundation(Grant No.202201)+1 种基金the National Natural Science Foundation of China(Grant No.62071164)the Open Fund of Information Materials and Intelligent Sensing Laboratory of Anhui Province(Anhui University)(Grant No.IMIS202214 and IMIS202102)。
文摘This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain classification.VGG-Net has a strong ability of deep feature extraction,which can fully extract the global deep features of different terrains in PolSAR images,so it is widely used in PolSAR terrain classification.However,VGG-Net ignores the local edge & shape features,resulting in incomplete feature representation of the PolSAR terrains,as a consequence,the terrain classification accuracy is not promising.In fact,edge and shape features play an important role in PolSAR terrain classification.To solve this problem,a new VGG network with HOG feature fusion was specifically proposed for high-precision PolSAR terrain classification.HOG-VGG extracts both the global deep semantic features and the local edge & shape features of the PolSAR terrains,so the terrain feature representation completeness is greatly elevated.Moreover,HOG-VGG optimally fuses the global deep features and the local edge & shape features to achieve the best classification results.The superiority of HOG-VGG is verified on the Flevoland,San Francisco and Oberpfaffenhofen datasets.Experiments show that the proposed HOG-VGG achieves much better PolSAR terrain classification performance,with overall accuracies of 97.54%,94.63%,and 96.07%,respectively.
文摘针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.
文摘重大装备制造中厚板机器人多层多道焊(multi-layer and multi-pass welding,MLMPW)一直是热点和难点,而实现机器人MLMPW的核心是对其熔池的获取、监控并分类.为了提高MLMPW的自动化和智能化,有必要开发一个熔池图像在线分类系统.针对焊接过程中的熔池图像提出了一种新的MLMPW熔池分类方法——基于视觉注意的(SENet)VGGNet熔池分类方法.为了提高效率和精度,引入迁移学习中的预训练模型到网络训练过程中.因为针对中厚板多层多道熔池研究较少,导致熔池公开数据集较少,为了应对这一问题,需要对数据集进行增广.结果表明,提出的模型可快速有效的对七类MLMPW熔池进行准确分类,预测精度可达到98.39%.
基金This paper is partially supported by the Royal Society International Exchanges Cost Share Award,UK(RP202G0230)Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)+3 种基金Hope Foundation for Cancer Research,UK(RM60G0680)British Heart Foundation Accelerator Award,UKSino-UK Industrial Fund,UK(RP202G0289)Global Challenges Research Fund(GCRF),UK(P202PF11).In addition,we acknowledge the help of Dr.Hemil Patel and Dr.Qinghua Zhou for their help in English correction.
文摘Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.
