典型文献
Visual-simulation region proposal and generative adversarial network based ground military target recognition
文献摘要：
Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications,but is disturbed by low-resolution and noisy-representation.In this paper,a recognition method,involving a novel visual attention mechanism-based Gabor region proposal sub-network(Gabor RPN)and improved refinement generative adversa-rial sub-network(GAN),is proposed.Novel central-peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN.Improved refinement GAN is used to solve the problem of blurry target classification,involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs.fake images but also the class of targets.A special recognition dataset for ground military target,named Ground Military Target Dataset(GMTD),is constructed.Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved,thus ensuring this algorithm a good engineering application prospect.
文献关键词：
中图分类号：
作者姓名：
Fan-jie Meng;Yong-qiang Li;Fa-ming Shao;Gai-hong Yuan;Ju-ying Dai
作者机构：
Department of Space Test and Launch,Noncommissioned Officer School,Space Engineering University,Beijing,102299,China;Department of Mechanical Engineering,College of Field Engineering,Army Engineering University of PLA,Nanjing,210007,China
文献出处：
引用格式：
[1]Fan-jie Meng;Yong-qiang Li;Fa-ming Shao;Gai-hong Yuan;Ju-ying Dai-.Visual-simulation region proposal and generative adversarial network based ground military target recognition)[J].防务技术,2022(11):2083-2096
A类：
adversa,GMTD
B类：
Visual,simulation,region,proposal,generative,adversarial,network,ground,military,recognition,Ground,plays,crucial,role,unmanned,equipment,grasping,battlefield,dynamics,applications,but,disturbed,by,low,resolution,noisy,representation,In,this,paper,method,involving,novel,visual,attention,mechanism,Gabor,sub,RPN,improved,refinement,GAN,proposed,Novel,central,peripheral,rivalry,color,filters,are,simulate,retinal,structures,taken,feature,extraction,convolutional,kernels,level,layer,accuracy,framework,training,efficiency,Improved,used,solve,problem,blurry,classification,generator,directly,generate,large,high,images,from,small,ones,discriminator,distinguish,not,only,real,fake,also,targets,special,dataset,named,Military,Target,Dataset,constructed,Experiments,performed,effectively,demonstrate,that,our,can,achieve,better,energy,saving,results,when,involved,thus,ensuring,algorithm,good,engineering,prospect
AB值：
0.611479
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