2025年 05期

摘要(Abstract)：

针对红外热像仪在检测温度和背景差别小的弱目标时，因设备灵敏度有限、目标到红外热像仪距离影响、噪声干扰以及原始温度数据变换为图像后像素颜色值动态变化范围较大等因素而导致检测性能显著下降的问题，利用红外热像仪原始数据，在获得同一目标距离红外热像仪不同距离时的灵敏度曲线基础上，提出基于随机灰度涨落场和3σ(σ为标准差)准则的低分辨率热成像弱目标检测算法。首先，读取热成像原始温度数据，测量和获取原始温度数据中目标距离红外热像仪在不同距离时的温度灵敏度曲线；其次，根据场景中温度与灰度的对应关系恢复为灰度图像；再次，建立场景的背景模型，并提出随机灰度涨落场模型；最后，结合灵敏度曲线及3σ准则消除灰度涨落对弱目标背景差分的影响，实现热成像场景中弱目标高效检测。结果表明：本方法能显著减少目标与设备间不同距离导致的像素动态变化影响，引入的随机灰度涨落场模型能够自适应地描述由相机内、因素引发的灰度波动现象，对游泳池中游泳人员的检测准确率大于95%,具有较好的鲁棒性和实用性。

关键词(KeyWords)：红外热成像;弱目标检测;随机灰度涨落场;背景差分

基金项目(Foundation):中央引导地方科技发展项目(YDZX2024078);; 山东省科技型中小企业创新能力提升工程(2022TSGC1047)

作者(Author): 辛国华,李志安,林道程,夏英杰,李金屏

DOI:10.13349/j.cnki.jdxbn.20250822.001

参考文献(References)：

[1]RAI M.Thermal imaging system and its real time applications:a survey[J].Journal of Engineering Technology,2018,6(2):290.

[2]韩金辉,魏艳涛,彭真明,等.红外弱小目标检测方法综述[J].红外与激光工程,2022,51(4):438

[3]杨兰兰.复杂背景下的弱小红外目标检测[D].北京:中国科学院光电技术研究所,2022.

[4]赵永强,饶元,董世鹏,等.深度学习目标检测方法综述[J].中国图象图形学报,2020,25(4):629.

[5]WANG S H,DU J P,CHAI J F,et al.Infrared small target detection algorithm based on potential regions proposal[C]//Fifth Symposium on Novel Optoelectronic Detection Technology and Application,October 24-26,2018,Xi’an,China.Washington,D.C.:Society of Photo-optical Instrumentation Engineers (SPIE),2019:110234R.

[6]QU Q Z,WANG Y L,LIU W J,et al.A false alarm controllable detection method based on CNN for sea-surface small targets[J].IEEE Geoscience and Remote Sensing Letters,2022,19 (1):4025705.

[7]刘俊明,孟卫华.融合全卷积神经网络和视觉显著性的红外小目标检测[J].光子学报,2020,49(7):46.

[8]SHI M,WANG H.Infrared dim and small target detection based on denoising autoencoder network[J].Mobile Networks and Applications,2020,25:1469.

[9]DEM I·R H S,AKAGüNDüZ E.Filter design for small target detection on infrared imagery using normalized-cross-correlation layer[J].Turkish Journal of Electrical Engineering and Computer Sciences,2020,28(1):302.

[10]代牮,赵旭,李连鹏,等.基于改进YOLOv5的复杂背景红外弱小目标检测算法[J].红外技术,2022,44(5):504.

[11]LEE J Y.A study of CR-Du NN based on the LSTM and DuCNN to predict infrared target feature and classify targets from the clutters[J].The Transactions of the Korean Institute of Electrical Engineers,2019,68(1):153.

[12]杨其利,周炳红,郑伟,等.注意力卷积长短时记忆网络的弱小目标轨迹检测[J].光学精密工程,2020,28(11):2535.

[13]WANG H,ZHOU L P,WANG L.Miss detection vs.false alarm:adversarial learning for small object Segmentation in infrared images[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV),October 27-November 2,2019,Seoul,Korea(South).New York:IEEE,2019:8508.

[14]ZHAO B,WANG C P,FU Q,et al.A novel pattern for infrared small target detection with generative adversarial network[J].IEEE Transactions on Geoscience and Remote Sensing,2021,59(5):4481.

[15]ZHU H,ZHANG J K,XU G X,et al.Balanced ring top-hat transformation for infrared small target detection with guided filter kernel[J].IEEE Transactions on Aerospace and Electronic Systems,2020,56(5):3892.

[16]ZHU H,LIU S M,DENG L Z,et al.Infrared small target detection via low-rank tensor completion with top-hat regularization[J].IEEE Transactions on Geoscience and Remote Sensing,2020,58(2):1004.

[17]CHEN Y H,ZHANG G P,MA Y J,et al.Small infrared target detection based on fast adaptive masking and scaling with iterative segmentation[J].IEEE Geoscience and Remote Sensing Letters,2022,19(1):7000605.

[18]CHEN C L P,LI H,WEI Y T,et al.A local contrast method for small infrared target detection[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(1):574.

[19]HAN J H,MORADI S,FARAMARZI I,et al.A local contrast method for infrared small-target detection utilizing a tri-layer window[J].IEEE Geoscience and Remote Sensing Letters,2020,17(10):1822.

[20]HAN J H,LIU C Y,LIU Y C,et al.Infrared small target detection utilizing the enhanced closest-mean background estimation[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2021,14(1):645.

[21]陈大海,莫林,易向阳.固定摄像头图像中测算目标距离和尺寸的算法[J].大众科技,2011,144(8):1.

[22]张斐然,董方,刘冰洁,等.“高分七号”卫星双线阵相机暗电流特性分析及校正[J].航天返回与遥感,2020,41(2):96.

[23]李晓瑜,马大中,付英杰.基于三帧差分混合高斯背景模型运动目标检测[J].吉林大学学报(信息科学版),2018,36(4):414.

[24]郁怀波,胡越黎,王佳乐,等.改进视觉背景提取模型的前景目标检测算法[J].信息与控制,2019,48(2):164.