基于深度卷积神经网络的红外小目标检测
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华中科技大学 人工智能与自动化学院 多谱信息处理技术国家级重点实验室

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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


Small target detection in infrared images using deep convolutional neural networks
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National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology

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    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。

    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.

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引用本文

吴双忱,左峥嵘.基于深度卷积神经网络的红外小目标检测[J].红外与毫米波学报,2019,38(3):371~380]. WU Shuang-Chen, ZUO Zheng-Rong. Small target detection in infrared images using deep convolutional neural networks[J]. J. Infrared Millim. Waves,2019,38(3):371~380.]

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  • 收稿日期:2018-05-07
  • 最后修改日期:2018-06-26
  • 录用日期:2018-06-28
  • 在线发布日期: 2019-07-02
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