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基于改进顶帽变换的红外弱小目标检测

张晶晶 曹思华 崔文楠 张涛

张晶晶, 曹思华, 崔文楠, 张涛. 基于改进顶帽变换的红外弱小目标检测[J]. 电子与信息学报, 2024, 46(1): 267-276. doi: 10.11999/JEIT221562
引用本文: 张晶晶, 曹思华, 崔文楠, 张涛. 基于改进顶帽变换的红外弱小目标检测[J]. 电子与信息学报, 2024, 46(1): 267-276. doi: 10.11999/JEIT221562
ZHANG Jingjing, CAO Sihua, CUI Wennan, ZHANG Tao. Improved Top-hat Transform–based Algorithm for Infrared Dim and Small Target Detection[J]. Journal of Electronics & Information Technology, 2024, 46(1): 267-276. doi: 10.11999/JEIT221562
Citation: ZHANG Jingjing, CAO Sihua, CUI Wennan, ZHANG Tao. Improved Top-hat Transform–based Algorithm for Infrared Dim and Small Target Detection[J]. Journal of Electronics & Information Technology, 2024, 46(1): 267-276. doi: 10.11999/JEIT221562

基于改进顶帽变换的红外弱小目标检测

doi: 10.11999/JEIT221562
基金项目: 中国科学院智能红外感知重点实验室开放课题(CAS-IIRP-2021-03)
详细信息
    作者简介:

    张晶晶:男,博士,副教授,博士生导师,研究方向为计算成像、图像处理

    曹思华:女,硕士生,研究方向为目标检测

    崔文楠:男,博士,研究员,硕士生导师,研究方向为光电探测、红外仿真、图像处理

    张涛:男,博士,研究员,博士生导师,研究方向为光电成像及微弱信号处理

    通讯作者:

    张晶晶 jingjingzhang@cug.edu.cn

  • 中图分类号: TN911.7; TP391

Improved Top-hat Transform–based Algorithm for Infrared Dim and Small Target Detection

Funds: Open project of Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences (CAS-IIRP-2021-03)
  • 摘要: 天空背景下的红外弱小目标检测技术较为成熟,但在近地复杂背景下,红外弱小目标的检测存在准确率不高、虚警目标多、实时性差的问题。针对以上问题,该文提出一种基于改进顶帽变换的红外弱小目标检测算法(OTHOLCM)。该算法采用基于改进顶帽变换的图像预处理算法(OTH),通过对不同灰度值的图像采取不同的策略针对性地处理图像,达到目标增强、背景抑制的效果。并在此基础上,采用基于改进多尺度局部对比度的红外弱小目标检测算法(OLCM),通过针对目标尺寸特点进行尺度设计,使得在保证算法实时性的基础上扩大目标尺寸检测范围。实验证明:OTHOLCM算法可以保证实时性并明显提高目标检测准确率、减少虚警目标数量。与3层模板局部差异度量算法(TTLDM)、基于边角感知的时空张量模型(ECASTT)等先进算法相比,OTHOLCM算法可使真阳性率分别提高近79%, 61%,假阳性率分别降低近77%, 73%,目标检测速度达到每秒25帧。
  • 图  1  改进白顶帽变换算法处理结果及其3维灰度图对比图

    图  2  不同场景和学习系数组合情况下的目标检测结果对比图

    图  3  OLCM算法和MLCM算法检测结果比较图

    图  4  先进目标检测算法目标检测结果对比图

    图  5  先进目标检测算法目标检测结果

    表  1  不同系数组合下OTHOLCM算法性能的对比

    实验组学习系数组合Precision(%)TPR(%)FPR(%)SNR
    1(0.1,0.6,0.3)12.3590.630.283.33
    2(0.2,0.6,0.3)9.9291.250.332.78
    3(0.3,0.6,0.3)9.5391.250.342.40
    4(0.1,0.5,0.3)11.6585.590.323.50
    5(0.1,0.7,0.3)10.8493.750.313.20
    6(0.1,0.6,0.2)12.3993.750.322.71
    7(0.1,0.6,0.4)10.5290.000.312.99
    下载: 导出CSV

    表  2  图像预处理算法性能对比结果

    指标SAMF[3]AF[4]CABF[10]DWTH[20]CWTH[7]IDCP[22]CWBTH[23]OTH
    时间(s)0.150.070.120.010.070.150.140.01
    SNR(dB)1.531.512.334.6241.945.025.855.52
    下载: 导出CSV

    表  3  经典目标检测算法性能对比结果

    指标IPI[13]PSTNN[14]Top-hat[7]MPCM[17]RLCM[18]MLCM[15]OLCM
    时间(s)3.5820.1020.0020.0391.7180.0400.053
    AUC0.5330.5240.5510.6240.7760.8040.851
    下载: 导出CSV

    表  4  先进算法和组合目标检测算法性能对比

    指标MLCMOLCMIDCPLCMCWBLCMOTHLCMIDCPOLCMCWBOLCMOTHOLCM
    时间(s)0.0400.0530.1400.1400.0430.1420.0320.045
    AUC0.8050.8520.8030.8330.8460.8520.8780.893
    下载: 导出CSV

    表  5  先进目标检测算法性能对比(%)

    指标OLCMOTHOLCMECASTT[24]TTLDM[19]
    TPR95.1795.3534.7616.82
    FPR14.9412.3585.1789.23
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-21
  • 修回日期:  2023-04-23
  • 网络出版日期:  2023-04-27
  • 刊出日期:  2024-01-17

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