Point target detection based on deep spatial-temporal convolution neural network
投稿时间:2020-01-10  修订日期:2020-08-21  download
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作者单位E-mail
李淼 国防科技大学 电子科学学院湖南 长沙 410073 lm8866@nudt.edu.cn 
林再平 国防科技大学 电子科学学院湖南 长沙 410073  
樊建鹏 国防科技大学 电子科学学院湖南 长沙 410073  
盛卫东 国防科技大学 电子科学学院湖南 长沙 410073  
李骏 国防科技大学 电子科学学院湖南 长沙 410073  
安玮 国防科技大学 电子科学学院湖南 长沙 410073  
李昕磊 西安卫星测控中心陕西 西安 710000  
Abstract:Point target detection in Infrared Search and Track (IRST) is a challenging task, due to less information. Traditional methods based on hand-crafted features are hard to finish detection intelligently. A novel deep spatial-temporal convolution neural network is proposed to suppress background and detect point targets. The proposed method is realized based on fully convolution network. So input of arbitrary size can be put into the network and correspondingly-sized output can be obtained. In order to meet the requirement of real time for practical application, the factorized technique is adopted. 3D convolution is decomposed into 2D convolution and 1D convolution, and it leads to signi?cantly less computation. Multi-weighted loss function is designed according to the relation between prediction error and detection performance for point target. Number-balance weight and intensity-balance weight are introduced to deal with the imbalanced sample distribution and imbalanced error distribution. The experimental results show that the proposed method can effectively suppress background clutters, and detect point targets with less runtime.
keywords:point target detection  infrared search and track (IRST)  background suppression  convolution neural network (CNN)  spatial-temporal detection
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Copyright:《Journal of Infrared And Millimeter Waves》