基于深度卷积神经网络的红外小目标检测
投稿时间:2018-05-07  修订日期:2018-06-26  点此下载全文
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作者单位E-mail
吴双忱 华中科技大学 自动化学院 m18571644738@163.com 
左峥嵘 华中科技大学 图像识别与人工智能研究所 多谱图像处理国家重点实验室 zhrzuo@hust.edu.cn 
基金项目:飞行器光学成像末制导快速数据处理与智能目标识别方法
中文摘要:提出了一种新的解决红外图像小目标检测问题的深度卷积网络,将对小目标的检测问题转化为对小目标位置分布的分类问题;检测网络由全卷积网络和分类网络组成,全卷积网络对红外小目标进行增强和初步筛选,实现红外图像的背景抑制,分类网络以原始图像和背景抑制后的图像为输入,对目标点后续筛选,网络中引入特征压缩提取网络(Squeeze-and-Excitation Networks)对特征图进行选择;实验验证了整个检测网络相对于传统小目标检测算法的优势,所提出的基于深度卷积神经网络的小目标检测方法对复杂背景下低信噪比且存在运动模糊的小目标具有很好的检测效果。
中文关键词:红外小目标检测  深度卷积网络  特征压缩提取网络  信噪比  运动模糊
 
Small target detection in infrared images using Deep Convolutional Neural Networks
Abstract:A new deep convolutional network for detecting small targets in infrared images is proposed. The problem of small targets detection is transformed into the classification of small targets’ location distribution. First, a Fully Convolutional Networks is used for enhancing and initially screening the small targets. After that, the original image and the background suppressed image are selected as the inputs for classification network which is used for the follow-up screening, and then the SEnet (Squeeze-and-Excitation Networks) is used to select the feature maps. The experimental results show that the detection network is superior to multiple typical infrared small target detection methods and has an excellent result on different signal-to-noise ratio,different scenes and motion blur targets.
keywords:Small  Target Detection  in infrared  images, Deep  Convolutional Networks,SEnet, signal-to-noise  ratio, motion  blur
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