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基于多尺度特征融合的红外小目标检测方法

王芳 李传强 伍博 于坤 金婵 陈亚珂 卢颖慧

王芳, 李传强, 伍博, 于坤, 金婵, 陈亚珂, 卢颖慧. 基于多尺度特征融合的红外小目标检测方法[J]. 红外技术, 2021, 43(7): 688-695.
引用本文: 王芳, 李传强, 伍博, 于坤, 金婵, 陈亚珂, 卢颖慧. 基于多尺度特征融合的红外小目标检测方法[J]. 红外技术, 2021, 43(7): 688-695.
WANG Fang, LI Chuanqiang, WU Bo, YU Kun, JIN Chan, CHEN Yake, LU Yinghui. Infrared Small Target Detection Method Based on Multi-Scale Feature Fusion[J]. Infrared Technology , 2021, 43(7): 688-695.
Citation: WANG Fang, LI Chuanqiang, WU Bo, YU Kun, JIN Chan, CHEN Yake, LU Yinghui. Infrared Small Target Detection Method Based on Multi-Scale Feature Fusion[J]. Infrared Technology , 2021, 43(7): 688-695.

基于多尺度特征融合的红外小目标检测方法

基金项目: 

河南省科技创新研究团队项目 21IRTSTHN011

国家自然科学基金项目 62075057

中国科学院界面物理技术重点实验 CASKL-IPT2003

详细信息
    作者简介:

    王芳(1972-),女,教授,主要研究方向为目标检测技术。E-mail: ffdd1012@163.com

    通讯作者:

    伍博(1980-),男,讲师,主要研究方向为计算机视觉。E-mail: wubo@htu.edu.cn

  • 中图分类号: TP39

Infrared Small Target Detection Method Based on Multi-Scale Feature Fusion

  • 摘要: 红外小目标检测因其探测距离远、抗干扰能力强等特点,在空中目标探测与跟踪系统中得到了广泛的应用。针对目前红外小目标检测算法在复杂背景下检测准确率低、虚警率高等缺点。提出了一种基于多尺度特征融合的端到端红外小目标检测模型(multi-scale feature fusion single shot multibox detecto,MFSSD)。考虑到红外小目标的特点,通过细化和融合特征图的方法提出了一种特征融合模块,通过SP模块提高特征图不同通道的相关性,3种不同序列红外图像的实验结果表明,该算法在红外小目标检测中的平均检测精度高达87.8%。与传统的多尺度目标检测算法相比,准确率和召回率都有显著提高。
  • 图  1  MFSSD算法网络结构图框架

    Figure  1.  MFSSD network structure diagram

    图  2  特征图调整过程

    Figure  2.  Feature map adjustment process

    图  3  FFM模块的网络结构图

    Figure  3.  FFM module network structure diagram

    图  4  SP模块网络结构图

    Figure  4.  SP module network structure diagram

    图  5  模型-1、2、3、4、5网络的损耗函数曲线

    Figure  5.  Loss functions curves of Model-1, 2, 3, 4, 5 networks

    图  6  模型-1、2、3、4、5的测试结果

    Figure  6.  Test results for Model-1, 2, 3, 4, 5

    图  7  红外小目标测试中的Recall与precision折线图

    Figure  7.  Recall versus precision graph in infrared small target test

    表  1  红外小目标数据集描述

    Table  1.   Details of the infrared small target dataset

    Name Total number Image resolution Detail
    Data1 399 256×256 The background is a sky back-ground
    with varying degrees of thermal noise and a single target
    Data2 100 256×256 Background is the intersection of sky
    and ground background, a single target
    Data3 998 256×256 The background is a sky back-ground with two
    targets and cross flying
    下载: 导出CSV

    表  2  实验中的比较算法

    Table  2.   The comparison algorithms in the experiment

    Model Model description
    1 SSD
    2 SSD+FFM(FFM module
    adopts up-sampling and down-sampling methods for fusion)
    3 SSD +FFM(FFM module
    adopts subpixel convolutional layer and path layer methods for fusion)
    4 SSD + FFM(FFM module
    adopts up-sampling and down-sampling methods for fusion) + SP module
    5(ours) SSD+FFM(FFM module
    adopts subpixel convolutional layer and path layer methods for fusion)+ SP module
    下载: 导出CSV

    表  3  不同网络算法的性能比较

    Table  3.   Comparison of algorithm performance of different networks

    Model Input Train Test Map Fps
    1 256 1297 200 82.5 29
    2 256 1297 200 85.5 23
    3 256 1297 200 86.2 25
    4 256 1297 200 86.1 15
    5 256 1297 200 87.8 17
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-24
  • 修回日期:  2021-05-21
  • 刊出日期:  2021-07-01

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