[1]ZHAO Qian,HAN Jinhui,XU Mao,et al.Infrared small-target detection utilizing background estimation combined with local contrast[J].CAAI Transactions on Intelligent Systems,2022,17(2):314-324.[doi:10.11992/tis.202101009]
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Infrared small-target detection utilizing background estimation combined with local contrast

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 2 Page number: 314-324 Column: 学术论文—机器感知与模式识别 Public date: 2022-03-05
Title:
Infrared small-target detection utilizing background estimation combined with local contrast
Author(s):
ZHAO Qian;  HAN Jinhui;  XU Mao;  CHEN Yuanyuan
College of Physics and Telecommunication, Zhoukou Normal University, Zhoukou 466001, China
Keywords:
IR small target; max-mean; background estimation; ratio-difference joint; local contrast; target detection; single frame detection; noise-resistant ability
CLC:
TP391.4
DOI:
10.11992/tis.202101009
Abstract:
In order to alleviate the interference of complex backgrounds in infrared small target detection, a new infrared small target detection method is proposed. The new method combines background estimation with local contrast to improve the detection performance of the algorithm. The main work includes the following aspects: First, a double-layer max-mean background estimation algorithm that is more suitable for small target detection is proposed to get a more accurate background benchmark. Then, a ratio-difference joint local contrast measure is proposed between the raw image and the estimated background to enhance the different sizes of true targets while simultaneously suppressing various types of complex backgrounds. The analysis and comparison in the experiment show its effectiveness and robustness.
References:
[1] LEUNG H, YOUNG A. Small target detection in clutter using recursive nonlinear prediction[J]. IEEE transactions on aerospace and electronic systems, 2000, 36(2): 713–718.
[2] GAO Chenqiang, ZHANG Tianqi, LI Qiang. Small infrared target detection using sparse ring representation[J]. IEEE aerospace and electronic systems magazine, 2012, 27(3): 21–30.
[3] KIM S. Target attribute-based false alarm rejection in small infrared target detection[C]//Proceedings of SPIE 8537, Image and Signal Processing for Remote Sensing XVIII. Edinburgh, United Kingdom, 2012: 85370G.
[4] HAN Jinhui, MA Yong, ZHOU Bo, et al. A robust infrared small target detection algorithm based on human visual system[J]. IEEE geoscience and remote sensing letters, 2014, 11(12): 2168–2172.
[5] ZHANG Biyin, ZHANG Tianxu, CAO Zhiqiang, et al. Fast new small-target detection algorithm based on a modified partial differential equation in infrared clutter[J]. Optical engineering, 2007, 46(10): 106401.
[6] QI Baojun, WU Tao, HE Hangen. Robust detection of small infrared objects in maritime scenarios using local minimum patterns and spatio-temporal context[J]. Optical engineering, 2012, 51(2): 027205.
[7] BAI Xiangzhi, ZHOU Fugen. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern recognition, 2010, 43(6): 2145–2156.
[8] Deshpande S D, Er M H, Ronda V, et al. Max-mean and max-median filters for detection of small targets[C]//Proceedings of SPIE 3809, Signal and Data Processing of Small Targets. Denver, United States, 1999: 74-83.
[9] CAO Yuan, LIU Ruiming, YANG Jie. Small Target detection using two-dimensional least mean square (TDLMS) filter based on neighborhood analysis[J]. International journal of infrared and millimeter waves, 2008, 29(2): 188–200.
[10] DING Hao, ZHAO Huijie. Adaptive method for the detection of infrared small target[J]. Optical engineering, 2015, 54(11): 113107.
[11] BI Yanguang, BAI Xiangzhi, JIN Ting, et al. Multiple feature analysis for infrared small target detection[J]. IEEE geoscience and remote sensing letters, 2017, 14(8): 1333–1337.
[12] GUO Jun, WU Yiquan, DAI Yimian. Small target detection based on reweighted infrared patch-image model[J]. IET image processing, 2018, 12(1): 70–79.
[13] ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE transactions on pattern analysis and machine intelligence, 1998, 20(11): 1254–1259.
[14] HAN Jinhui, LIANG Kun, ZHOU Bo, et al. Infrared small target detection utilizing the multiscale relative local contrast measure[J]. IEEE geoscience and remote sensing letters, 2018, 15(4): 612–616.
[15] SHAO Xiaopeng, FAN Hua, LU Guangxu, et al. An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system[J]. Infrared physics & technology, 2012, 55(5): 403–408.
[16] DENG Yaping, WANG Min. Infrared dim and small target detection based on the human visual attention mechanism[M]//WANG W. Proceedings of the Second International Conference on Mechatronics and Automatic Control. Cham: Springer, 2015: 1153-1161.
[17] HAN Jinhui, MA Yong, HUANG Jun, et al. An infrared small target detecting algorithm based on human visual system[J]. IEEE geoscience and remote sensing letters, 2016, 13(3): 452–456.
[18] XIE Kai, FU Keren, ZHOU Tao, et al. Small target detection based on accumulated center-surround difference measure[J]. Infrared physics & technology, 2014, 67: 229–236.
[19] WEI Yantao, YOU Xinge, LI Hong. Multiscale patch-based contrast measure for small infrared target detection[J]. Pattern recognition, 2016, 58: 216–226.
[20] CHEN C L P, LI Hong, WEI Yantao, et al. A local contrast method for small infrared target detection[J]. IEEE transactions on geoscience and remote sensing, 2014, 52(1): 574–581.
[21] QIN Yao, LI Biao. Effective infrared small target detection utilizing a novel local contrast method[J]. IEEE geoscience and remote sensing letters, 2016, 13(12): 1890–1894.
[22] DENG He, SUN Xianping, LIU Maili, et al. Entropy-based window selection for detecting dim and small infrared targets[J]. Pattern recognition, 2017, 61: 66–77.
[23] XIA Tian, TANG Yuanyan, et al. Biologically inspired small infrared target detection using local contrast mechanisms[J]. International journal of wavelets, 2015, 13(4): 1550025.
[24] CUI Zheng, YANG Jingli, JIANG Shouda, et al. An infrared small target detection algorithm based on high-speed local contrast method[J]. Infrared physics & technology, 2016, 76: 474–481.
[25] CHEN Yuwen, XIN Yunhong. An efficient infrared small target detection method based on visual contrast mechanism[J]. IEEE geoscience and remote sensing letters, 2016, 13(7): 962–966.
[26] 马旗, 朱斌, 程正东, 等. 基于双通道的快速低空无人机检测识别方法[J]. 光学学报, 2019, 39(12): 1210002
MA Qi, ZHU Bin, CHENG Zhengdong, et al. Detection and recognition method of fast low-altitude unmanned aerial vehicle based on dual channel[J]. Acta optica sinica, 2019, 39(12): 1210002
[27] HOU Qingyu, WANG Zhipeng, TAN Fanjiao, et al. RISTDnet: robust infrared small target detection network[EB/OL]. (2021-01-22) [2021-05-26]. https://ieeexplore.ieee.org/document/9334387.
[28] DAI Yimian, WU Yiquan, ZHOU Fei, et al. Attentional local contrast networks for infrared small target detection[EB/OL]. (2020-12-25) [2021-05-26]. https://ieeexplore.ieee.org/abstract/document/9314219.
[29] ZHANG Wei, CONG Mingyu, WANG Liping. Algorithms for optical weak small targets detection and tracking: review[C]//International Conference on Neural Networks and Signal Processing. Nanjing, China, 2003: 643-647.
[30] WANG Xin, LYU Guofang, XU Lizhong. Infrared dim target detection based on visual attention[J]. Infrared physics & technology, 2012, 55(6): 513–521.
[31] DENG He, SUN Xianping, LIU Maili, et al. Small infrared target detection based on weighted local difference measure[J]. IEEE transactions on geoscience and remote sensing, 2016, 54(7): 4204–4214.
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